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Repository: rerun-io/simplerecon
Branch: main
Commit: 2a3778a8c89f
Files: 92
Total size: 49.9 MB
Directory structure:
gitextract_6sfl0utb/
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── configs/
│ ├── data/
│ │ ├── 7scenes_default.yaml
│ │ ├── 7scenes_dense.yaml
│ │ ├── colmap_dense.yaml
│ │ ├── neucon_arkit_default.yaml
│ │ ├── neucon_arkit_dense.yaml
│ │ ├── scannet_default_test.yaml
│ │ ├── scannet_default_train.yaml
│ │ ├── scannet_default_val.yaml
│ │ ├── scannet_dense_test.yaml
│ │ ├── scannet_dense_val.yaml
│ │ ├── scanniverse_dense.yaml
│ │ ├── vdr_default.yaml
│ │ ├── vdr_dense.yaml
│ │ ├── vdr_dense_offline.yaml
│ │ └── vdr_dense_rerun.yaml
│ └── models/
│ ├── dot_product_model.yaml
│ └── hero_model.yaml
├── data_scripts/
│ ├── 7scenes_preprocessing.py
│ ├── IOS_LOGGER_ARKIT_README.md
│ ├── generate_test_tuples.py
│ ├── generate_train_tuples.py
│ ├── ios_logger_preprocessing.py
│ ├── precompute_valid_frames.py
│ └── scannet_wrangling_scripts/
│ ├── LICENSE
│ ├── README.md
│ ├── SensorData.py
│ ├── download_scannet.py
│ ├── env.yml
│ ├── reader.py
│ └── splits/
│ ├── scannetv2_test.txt
│ ├── scannetv2_train.txt
│ └── scannetv2_val.txt
├── data_splits/
│ ├── 7Scenes/
│ │ └── dvmvs_split/
│ │ ├── dvmvs_test_split.txt
│ │ └── test_eight_view_deepvmvs.txt
│ ├── ScanNetv2/
│ │ ├── dvmvs_split/
│ │ │ ├── dvmvs_train.txt
│ │ │ ├── dvmvs_val.txt
│ │ │ ├── test_eight_view_deepvmvs.txt
│ │ │ └── test_eight_view_deepvmvs_dense.txt
│ │ └── standard_split/
│ │ ├── scannetv2_test.txt
│ │ ├── scannetv2_train.txt
│ │ ├── scannetv2_val.txt
│ │ ├── test_eight_view_deepvmvs.txt
│ │ ├── test_eight_view_deepvmvs_dense.txt
│ │ ├── test_eight_view_deepvmvs_offline.txt
│ │ ├── train_eight_view_deepvmvs.txt
│ │ └── val_eight_view_deepvmvs.txt
│ ├── arkit/
│ │ ├── scans.txt
│ │ ├── test_eight_view_deepvmvs.txt
│ │ └── test_eight_view_deepvmvs_dense.txt
│ └── vdr/
│ ├── scans.txt
│ ├── test_eight_view_deepvmvs.txt
│ ├── test_eight_view_deepvmvs_dense.txt
│ └── test_eight_view_deepvmvs_dense_offline.txt
├── experiment_modules/
│ └── depth_model.py
├── losses.py
├── modules/
│ ├── cost_volume.py
│ ├── layers.py
│ └── networks.py
├── options.py
├── pc_fusion.py
├── pixi.toml
├── requirements.txt
├── rerun_visualize_live_meshing.py
├── simplerecon_env.yml
├── sr_datasets/
│ ├── arkit_dataset.py
│ ├── colmap_dataset.py
│ ├── generic_mvs_dataset.py
│ ├── scannet_dataset.py
│ ├── scanniverse_dataset.py
│ ├── seven_scenes_dataset.py
│ └── vdr_dataset.py
├── test.py
├── tools/
│ ├── fusers_helper.py
│ ├── keyframe_buffer.py
│ ├── mesh_renderer.py
│ ├── torch_point_cloud_fusion.py
│ └── tsdf.py
├── train.py
├── utils/
│ ├── dataset_utils.py
│ ├── generic_utils.py
│ ├── geometry_utils.py
│ ├── metrics_utils.py
│ └── visualization_utils.py
├── visualization_scripts/
│ ├── generate_gt_min_max_cache.py
│ ├── load_meshes_and_include_normals.py
│ └── visualize_scene_depth_output.py
├── visualize_live_meshing.py
└── weights/
└── strip_checkpoint.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitattributes
================================================
# GitHub syntax highlighting
pixi.lock linguist-language=YAML
================================================
FILE: .gitignore
================================================
*.pyc
.vscode/
debug_scripts/
logs/
logs
models/*.ckpt
weights/*.ckpt
**__pycache__**
# pixi environments
.pixi
data/*
OUTPUT_PATH/*
================================================
FILE: LICENSE
================================================
Copyright © Niantic, Inc. 2022. Patent Pending.
All rights reserved.
================================================================================
This Software is licensed under the terms of the following SimpleRecon license
which allows for non-commercial use only. For any other use of the software not
covered by the terms of this license, please contact partnerships@nianticlabs.com
================================================================================
SimpleRecon License
This Agreement is made by and between the Licensor and the Licensee as
defined and identified below.
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In this Agreement (“the Agreement”) the following words shall have the
following meanings:
"Authors" shall mean M. Sayed, J. Gibson, J. Watson, V. Prisacariu,
M. Firman, C. Godard
"Licensee" Shall mean the person or organization agreeing to use the
Software in accordance with these terms and conditions.
"Licensor" shall mean Niantic Inc., a company organized and existing under
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"Software" shall mean the SimpleRecon Software uploaded by Licensor to the
GitHub repository at https://github.com/nianticlabs/SimpleRecon
on September 1st 2022 in source code or object code form and any
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================================================
FILE: README.md
================================================
# SimpleRecon: 3D Reconstruction Without 3D Convolutions
This is the reference PyTorch implementation for training and testing MVS depth estimation models which has been modified to use [Rerun](https://github.com/rerun-io/rerun) for visualization.
https://github.com/nianticlabs/simplerecon/assets/25287427/1d32d2b2-61fe-4007-ac86-39134a0af2e5
## Run using `pixi`
The easiest way to get started is to install [pixi](https://prefix.dev/docs/pixi/overview).
The pixi environment described in `pixi.toml` contains all of the dependencies, including the rerun viewer,
allowing you to setup your development environment and download demo data
This will work with Linux and Mac
Run the following in order
1`pixi run download-data`
2`pixi run vdr`
> **SimpleRecon: 3D Reconstruction Without 3D Convolutions**
>
> [Mohamed Sayed](https://masayed.com), [John Gibson](https://www.linkedin.com/in/john-e-gibson-ii/), [Jamie Watson](https://www.linkedin.com/in/jamie-watson-544825127/), [Victor Adrian Prisacariu](https://www.robots.ox.ac.uk/~victor/), [Michael Firman](http://www.michaelfirman.co.uk), and [Clément Godard](http://www0.cs.ucl.ac.uk/staff/C.Godard/)
>
> [Paper, ECCV 2022 (arXiv pdf)](https://arxiv.org/abs/2208.14743), [Supplemental Material](https://nianticlabs.github.io/simplerecon/resources/SimpleRecon_supp.pdf), [Project Page](https://nianticlabs.github.io/simplerecon/), [Video](https://youtu.be/3LP8jp45Ef8)
<p align="center">
<img src="media/teaser.jpeg" alt="example output" width="720" />
</p>
https://user-images.githubusercontent.com/14994206/187824325-e1878d77-2f14-4ea9-bc02-3fded1bb1851.mp4
https://user-images.githubusercontent.com/14994206/189788536-5fa8a1b5-ae8b-4f64-92d6-1ff1abb03eaf.mp4
This code is for non-commercial use; please see the [license file](LICENSE) for terms. If you do find any part of this codebase helpful, please cite our paper using the BibTex below and link this repo. Thanks!
## 🆕 Updates
09/03/2023: Added kornia version to the environments file to fix kornia typing issue. (thanks @natesimon!)
26/01/2023: The license has been modified to make running the model for academic reasons easier. Please the LICENSE file for the exact details.
There is an update as of 31/12/2022 that fixes slightly wrong intrinsics, flip augmentation for the cost volume, and a
numerical precision bug in projection. All scores improve. You will need to update your forks and use new weights. See [Bug Fixes](#-bug-fixes).
Precomputed scans for online default frames are here: https://drive.google.com/drive/folders/1dSOFI9GayYHQjsx4I_NG0-3ebCAfWXjV?usp=share_link
## Table of Contents
* [🗺️ Overview](#%EF%B8%8F-overview)
* [⚙️ Setup](#%EF%B8%8F-setup)
* [📦 Models](#-models)
* [🚀 Speed](#-speed)
* [📝 TODOs:](#-todos)
* [🏃 Running out of the box!](#-running-out-of-the-box)
* [💾 ScanNetv2 Dataset](#-scannetv2-dataset)
* [🖼️🖼️🖼️ Frame Tuples](#%EF%B8%8F%EF%B8%8F%EF%B8%8F-frame-tuples)
* [📊 Testing and Evaluation](#-testing-and-evaluation)
* [👉☁️ Point Cloud Fusion](#%EF%B8%8F-point-cloud-fusion)
* [📊 Mesh Metrics](#-mesh-metrics)
* [⏳ Training](#-training)
+ [🎛️ Finetuning a pretrained model](#%EF%B8%8F-finetuning-a-pretrained-model)
* [🔧 Other training and testing options](#-other-training-and-testing-options)
* [✨ Visualization](#-visualization)
* [📝🧮👩💻 Notation for Transformation Matrices](#-notation-for-transformation-matrices)
* [🗺️ World Coordinate System](#%EF%B8%8F-world-coordinate-system)
* [🐜🔧 Bug Fixes](#-bug-fixes)
* [🗺️💾 COLMAP Dataset](#%EF%B8%8F-colmap-dataset)
* [🙏 Acknowledgements](#-acknowledgements)
* [📜 BibTeX](#-bibtex)
* [👩⚖️ License](#%EF%B8%8F-license)
## 🗺️ Overview
SimpleRecon takes as input posed RGB images, and outputs a depth map for a target image.
## ⚙️ Setup
### Conda
Assuming a fresh [Anaconda](https://www.anaconda.com/download/) distribution, you can install dependencies with:
```shell
conda env create -f simplerecon_env.yml
```
We ran our experiments with PyTorch 1.10, CUDA 11.3, Python 3.9.7 and Debian GNU/Linux 10.
### virtualenv + pip
(Last tested with Python 3.10.9, torch
Create a virtualenv and install dependencies.
```
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
```
## 📦 Models
Download a pretrained model into the `weights/` folder.
We provide the following models (scores are with online default keyframes):
| `--config` | Model | Abs Diff↓| Sq Rel↓ | delta < 1.05↑| Chamfer↓ | F-Score↑ |
|-------------|----------|--------------------|---------|---------|--------------|----------|
| [`hero_model.yaml`](https://drive.google.com/file/d/1hCuKZjEq-AghrYAmFxJs_4eeixIlP488/view?usp=sharing) | Metadata + Resnet Matching | 0.0868 | 0.0127 | 74.26 | 5.69 | 0.680 |
| [`dot_product_model.yaml`](https://drive.google.com/file/d/13lW-VPgsl2eAo95E87RKWoK8KUZelkUK/view?usp=sharing) | Dot Product + Resnet Matching | 0.0910 | 0.0134 | 71.90 | 5.92 | 0.667 |
`hero_model` is the one we use in the paper as **Ours**
## 🚀 Speed
| `--config` | Model | Inference Speed (`--batch_size 1`) | Inference GPU memory | Approximate training time |
|------------|------------|------------|-------------------------|-----------------------------|
| `hero_model` | Hero, Metadata + Resnet | 130ms / 70ms (speed optimized) | 2.6GB / 5.7GB (speed optimized) | 36 hours |
| `dot_product_model` | Dot Product + Resnet | 80ms | 2.6GB | 36 hours |
With larger batches speed increases considerably. With batch size 8 on the non-speed optimized model, the latency drops to
~40ms.
## 📝 TODOs:
- [x] Simple scan for folks to quickly try the code, instead of downloading the ScanNetv2 test scenes. DONE
- [x] ScanNetv2 extraction, ~~ETA 10th October~~ DONE
- [ ] FPN model weights.
- ~~[ ] Tutorial on how to use Scanniverse data, ETA 5th October 10th October 20th October~~ At present there is no publically available way of exporting scans from Scanniverse. You'll have to use ios-logger; NeuralRecon have a good tutorial on [this](https://github.com/zju3dv/NeuralRecon/blob/master/DEMO.md), and a dataloader that accepts the processed format is at ```datasets/arkit_dataset.py```. UPDATE: There is now a quick readme [data_scripts/IOS_LOGGER_ARKIT_README.md](data_scripts/IOS_LOGGER_ARKIT_README.md) for how to process and run inference an ios-logger scan using the script at ```data_scripts/ios_logger_preprocessing.py```.
## 🏃 Running out of the box!
We've now included two scans for people to try out immediately with the code. You can download these scans [from here](https://drive.google.com/file/d/1x-auV7vGCMdu5yZUMPcoP83p77QOuasT/view?usp=sharing).
Steps:
1. Download weights for the `hero_model` into the weights directory.
2. Download the scans and unzip them to a directory of your choosing.
3. Modify the value for the option `dataset_path` in `configs/data/vdr_dense.yaml` to the base path of the unzipped vdr folder.
4. You should be able to run it! Something like this will work:
```bash
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config_file configs/data/vdr_dense.yaml \
--num_workers 8 \
--batch_size 2 \
--fast_cost_volume \
--run_fusion \
--depth_fuser open3d \
--fuse_color \
--dump_depth_visualization
```
for visualizing with rerun use the following command
```bash
CUDA_VISIBLE_DEVICES=0 python rerun_visualize_live_meshing.py \
--name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config_file configs/data/vdr_dense.yaml \
--num_workers 8 \
--run_fusion \
--depth_fuser open3d \
--fuse_color
```
This will output meshes, quick depth viz, and socres when benchmarked against LiDAR depth under `OUTPUT_PATH`.
This command uses `vdr_dense.yaml` which will generate depths for every frame and fuse them into a mesh. In the paper we report scores with fused keyframes instead, and you can run those using `vdr_default.yaml`. You can also use `dense_offline` tuples by instead using `vdr_dense_offline.yaml`.
See the section below on testing and evaluation. Make sure to use the correct config flags for datasets.
## 💾 ScanNetv2 Dataset
~~Please follow the instructions [here](https://github.com/ScanNet/ScanNet) to download the dataset. This dataset is quite big (>2TB), so make sure you have enough space, especially for extracting files.~~
~~Once downloaded, use this [script](https://github.com/ScanNet/ScanNet/tree/master/SensReader/python) to export raw sensor data to images and depth files.~~
We've written a quick tutorial and included modified scripts to help you with downloading and extracting ScanNetv2. You can find them at [data_scripts/scannet_wrangling_scripts/](data_scripts/scannet_wrangling_scripts)
You should change the `dataset_path` config argument for ScanNetv2 data configs at `configs/data/` to match where your dataset is.
The codebase expects ScanNetv2 to be in the following format:
dataset_path
scans_test (test scans)
scene0707
scene0707_00_vh_clean_2.ply (gt mesh)
sensor_data
frame-000261.pose.txt
frame-000261.color.jpg
frame-000261.color.512.png (optional, image at 512x384)
frame-000261.color.640.png (optional, image at 640x480)
frame-000261.depth.png (full res depth, stored scale *1000)
frame-000261.depth.256.png (optional, depth at 256x192 also
scaled)
scene0707.txt (scan metadata and image sizes)
intrinsic
intrinsic_depth.txt
intrinsic_color.txt
...
scans (val and train scans)
scene0000_00
(see above)
scene0000_01
....
In this example `scene0707.txt` should contain the scan's metadata:
colorHeight = 968
colorToDepthExtrinsics = 0.999263 -0.010031 0.037048 ........
colorWidth = 1296
depthHeight = 480
depthWidth = 640
fx_color = 1170.187988
fx_depth = 570.924255
fy_color = 1170.187988
fy_depth = 570.924316
mx_color = 647.750000
mx_depth = 319.500000
my_color = 483.750000
my_depth = 239.500000
numColorFrames = 784
numDepthFrames = 784
numIMUmeasurements = 1632
`frame-000261.pose.txt` should contain pose in the form:
-0.384739 0.271466 -0.882203 4.98152
0.921157 0.0521417 -0.385682 1.46821
-0.0587002 -0.961035 -0.270124 1.51837
`frame-000261.color.512.png` and `frame-000261.color.640.png` are precached resized versions of the original image to save load and compute time during training and testing. `frame-000261.depth.256.png` is also a
precached resized version of the depth map.
All resized precached versions of depth and images are nice to have but not
required. If they don't exist, the full resolution versions will be loaded, and downsampled on the fly.
## 🖼️🖼️🖼️ Frame Tuples
By default, we estimate a depth map for each keyframe in a scan. We use DeepVideoMVS's heuristic for keyframe separation and construct tuples to match. We use the depth maps at these keyframes for depth fusion. For each keyframe, we associate a list of source frames that will be used to build the cost volume. We also use dense tuples, where we predict a depth map for each frame in the data, and not just at specific keyframes; these are mostly used for visualization.
We generate and export a list of tuples across all scans that act as the dataset's elements. We've precomputed these lists and they are available at `data_splits` under each dataset's split. For ScanNet's test scans they are at `data_splits/ScanNetv2/standard_split`. Our core depth numbers are computed using `data_splits/ScanNetv2/standard_split/test_eight_view_deepvmvs.txt`.
Here's a quick taxonamy of the type of tuples for test:
- `default`: a tuple for every keyframe following DeepVideoMVS where all source frames are in the past. Used for all depth and mesh evaluation unless stated otherwise. For ScanNet use `data_splits/ScanNetv2/standard_split/test_eight_view_deepvmvs.txt`.
- `offline`: a tuple for every frame in the scan where source frames can be both in the past and future relative to the current frame. These are useful when a scene is captured offline, and you want the best accuracy possible. With online tuples, the cost volume will contain empty regions as the camera moves away and all source frames lag behind; however with offline tuples, the cost volume is full on both ends, leading to a better scale (and metric) estimate.
- `dense`: an online tuple (like default) for every frame in the scan where all source frames are in the past. For ScanNet this would be `data_splits/ScanNetv2/standard_split/test_eight_view_deepvmvs_dense.txt`.
- `offline`: an offline tuple for every keyframefor every keyframe in the scan.
For the train and validation sets, we follow the same tuple augmentation strategy as in DeepVideoMVS and use the same core generation script.
If you'd like to generate these tuples yourself, you can use the scripts at `data_scripts/generate_train_tuples.py` for train tuples and `data_scripts/generate_test_tuples.py` for test tuples. These follow the same config format as `test.py` and will use whatever dataset class you build to read pose informaiton.
Example for test:
```bash
# default tuples
python ./data_scripts/generate_test_tuples.py
--data_config configs/data/scannet_default_test.yaml
--num_workers 16
# dense tuples
python ./data_scripts/generate_test_tuples.py
--data_config configs/data/scannet_dense_test.yaml
--num_workers 16
```
Examples for train:
```bash
# train
python ./data_scripts/generate_train_tuples.py
--data_config configs/data/scannet_default_train.yaml
--num_workers 16
# val
python ./data_scripts/generate_val_tuples.py
--data_config configs/data/scannet_default_val.yaml
--num_workers 16
```
These scripts will first check each frame in the dataset to make sure it has an existing RGB frame, an existing depth frame (if appropriate for the dataset), and also an existing and valid pose file. It will save these `valid_frames` in a text file in each scan's folder, but if the directory is read only, it will ignore saving a `valid_frames` file and generate tuples anyway.
## 📊 Testing and Evaluation
You can use `test.py` for inferring and evaluating depth maps and fusing meshes.
All results will be stored at a base results folder (results_path) at:
opts.output_base_path/opts.name/opts.dataset/opts.frame_tuple_type/
where opts is the `options` class. For example, when `opts.output_base_path` is `./results`, `opts.name` is `HERO_MODEL`,
`opts.dataset` is `scannet`, and `opts.frame_tuple_type` is `default`, the output directory will be
./results/HERO_MODEL/scannet/default/
Make sure to set `--opts.output_base_path` to a directory suitable for you to store results.
`--frame_tuple_type` is the type of image tuple used for MVS. A selection should
be provided in the `data_config` file you used.
By default `test.py` will attempt to compute depth scores for each frame and provide both frame averaged and scene averaged metrics. The script will save these scores (per scene and totals) under `results_path/scores`.
We've done our best to ensure that a torch batching bug through the matching
encoder is fixed for (<10^-4) accurate testing by disabling image batching
through that encoder. Run `--batch_size 4` at most if in doubt, and if
you're looking to get as stable as possible numbers and avoid PyTorch
gremlins, use `--batch_size 1` for comparison evaluation.
If you want to use this for speed, set `--fast_cost_volume` to True. This will
enable batching through the matching encoder and will enable an einops
optimized feature volume.
```bash
# Example command to just compute scores
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/scannet_default_test.yaml \
--num_workers 8 \
--batch_size 4;
# If you'd like to get a super fast version use:
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/scannet_default_test.yaml \
--num_workers 8 \
--fast_cost_volume \
--batch_size 2;
```
This script can also be used to perform a few different auxiliary tasks,
including:
**TSDF Fusion**
To run TSDF fusion provide the `--run_fusion` flag. You have two choices for
fusers
1) `--depth_fuser ours` (default) will use our fuser, whose meshes are used
in most visualizations and for scores. This fuser does not support
color. We've provided a custom branch of scikit-image with our custom
implementation of `measure.matching_cubes` that allows single walled. We use
single walled meshes for evaluation. If this is isn't important to you, you
can set the export_single_mesh to `False` for call to `export_mesh` in `test.py`.
2) `--depth_fuser open3d` will use the open3d depth fuser. This fuser
supports color and you can enable this by using the `--fuse_color` flag.
By default, depth maps will be clipped to 3m for fusion and a tsdf
resolution of 0.04m<sup>3</sup> will be used, but you can change that by changing both
`--max_fusion_depth` and `--fusion_resolution`
You can optionnally ask for predicted depths used for fusion to be masked
when no vaiid MVS information exists using `--mask_pred_depths`. This is not
enabled by default.
You can also fuse the best guess depths from the cost volume before the
cost volume encoder-decoder that introduces a strong image prior. You can do this by using
`--fusion_use_raw_lowest_cost`.
Meshes will be stored under `results_path/meshes/`.
```bash
# Example command to fuse depths to get meshes
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/scannet_default_test.yaml \
--num_workers 8 \
--run_fusion \
--batch_size 8;
```
**Cache depths**
You can optionally store depths by providing the `--cache_depths` flag.
They will be stored at `results_path/depths`.
```bash
# Example command to compute scores and cache depths
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/scannet_default_test.yaml \
--num_workers 8 \
--cache_depths \
--batch_size 8;
# Example command to fuse depths to get color meshes
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/scannet_default_test.yaml \
--num_workers 8 \
--run_fusion \
--depth_fuser open3d \
--fuse_color \
--batch_size 4;
```
**Quick viz**
There are other scripts for deeper visualizations of output depths and
fusion, but for quick export of depth map visualization you can use
`--dump_depth_visualization`. Visualizations will be stored at `results_path/viz/quick_viz/`.
```bash
# Example command to output quick depth visualizations
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/scannet_default_test.yaml \
--num_workers 8 \
--dump_depth_visualization \
--batch_size 4;
```
## 👉☁️ Point Cloud Fusion
We also allow point cloud fusion of depth maps using the fuser from 3DVNet's [repo](https://github.com/alexrich021/3dvnet/blob/main/mv3d/eval/pointcloudfusion_custom.py).
```bash
# Example command to fuse depths into point clouds.
CUDA_VISIBLE_DEVICES=0 python pc_fusion.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/scannet_dense_test.yaml \
--num_workers 8 \
--batch_size 4;
```
Change `configs/data/scannet_dense_test.yaml` to `configs/data/scannet_default_test.yaml` to use keyframes only if you don't want to wait too long.
## 📊 Mesh Metrics
We use TransformerFusion's [mesh evaluation](https://github.com/AljazBozic/TransformerFusion/blob/main/src/evaluation/eval.py) for our main results table but set the seed to a fixed value for consistency when randomly sampling meshes. We also report mesh metrics using NeuralRecon's [evaluation](https://github.com/zju3dv/NeuralRecon/blob/master/tools/evaluation.py) in the supplemental material.
For point cloud evaluation, we use TransformerFusion's code but load in a point cloud in place of sampling a mesh's surface.
## ⏳ Training
By default models and tensorboard event files are saved to `~/tmp/tensorboard/<model_name>`.
This can be changed with the `--log_dir` flag.
We train with a batch_size of 16 with 16-bit precision on two A100s on the default ScanNetv2 split.
Example command to train with two GPUs:
```shell
CUDA_VISIBLE_DEVICES=0,1 python train.py --name HERO_MODEL \
--log_dir logs \
--config_file configs/models/hero_model.yaml \
--data_config configs/data/scannet_default_train.yaml \
--gpus 2 \
--batch_size 16;
```
The code supports any number of GPUs for training.
You can specify which GPUs to use with the `CUDA_VISIBLE_DEVICES` environment.
All our training runs were performed on two NVIDIA A100s.
**Different dataset**
You can train on a custom MVS dataset by writing a new dataloader class which inherits from `GenericMVSDataset` at `datasets/generic_mvs_dataset.py`. See the `ScannetDataset` class in `datasets/scannet_dataset.py` or indeed any other class in `datasets` for an example.
### 🎛️ Finetuning a pretrained model
To finetune, simple load a checkpoint (not resume!) and train from there:
```shell
CUDA_VISIBLE_DEVICES=0 python train.py --config configs/models/hero_model.yaml
--data_config configs/data/scannet_default_train.yaml
--load_weights_from_checkpoint weights/hero_model.ckpt
```
Change the data configs to whatever dataset you want to finetune to.
## 🔧 Other training and testing options
See `options.py` for the range of other training options, such as learning rates and ablation settings, and testing options.
## ✨ Visualization
Other than quick depth visualization in the `test.py` script, there are two scripts for visualizing depth output.
The first is `visualization_scripts/visualize_scene_depth_output.py`. This will produce a video with color images of the reference and source frames, depth prediction, cost volume estimate, GT depth, and estimated normals from depth. The script assumes you have cached depth output using `test.py` and accepts the same command template format as `test.py`:
```shell
# Example command to get visualizations for dense frames
CUDA_VISIBLE_DEVICES=0 python ./visualization_scripts/visualize_scene_depth_output.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--data_config configs/data/scannet_dense_test.yaml \
--num_workers 8;
```
where `OUTPUT_PATH` is the base results directory for SimpleRecon (what you used for test to begin with). You could optionally run `.visualization_scripts/generate_gt_min_max_cache.py` before this script to get a scene average for the min and max depth values used for colormapping; if those aren't available, the script will use 0m and 5m for colomapping min and max.
The second allows a live visualization of meshing. This script will use cached depth maps if available, otherwise it will use the model to predict them before fusion. The script will iteratively load in a depth map, fuse it, save a mesh file at this step, and render this mesh alongside a camera marker for the birdseye video, and from the point of view of the camera for the fpv video.
```shell
# Example command to get live visualizations for mesh reconstruction
CUDA_VISIBLE_DEVICES=0 python visualize_live_meshing.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/scannet_dense_test.yaml \
--num_workers 8;
```
By default the script will save meshes to an intermediate location, and you can optionally load those meshes to save time when visualizing the same meshes again by passing `--use_precomputed_partial_meshes`. All intermediate meshes will have had to be computed on the previous run for this to work.
## 📝🧮👩💻 Notation for Transformation Matrices
__TL;DR:__ `world_T_cam == world_from_cam`
This repo uses the notation "cam_T_world" to denote a transformation from world to camera points (extrinsics). The intention is to make it so that the coordinate frame names would match on either side of the variable when used in multiplication from *right to left*:
cam_points = cam_T_world @ world_points
`world_T_cam` denotes camera pose (from cam to world coords). `ref_T_src` denotes a transformation from a source to a reference view.
Finally this notation allows for representing both rotations and translations such as: `world_R_cam` and `world_t_cam`
## 🗺️ World Coordinate System
This repo is geared towards ScanNet, so while its functionality should allow for any coordinate system (signaled via input flags), the model weights we provide assume a ScanNet coordinate system. This is important since we include ray information as part of metadata. Other datasets used with these weights should be transformed to the ScanNet system. The dataset classes we include will perform the appropriate transforms.
## 🐜🔧 Bug Fixes
### **Update 31/12/2022:**
There are a few bugs addressed in this update, you will need to update your forks and use new weights from the table near the beginning of this README. You will also need to make sure you have the correct intrinsics files extracted using the reader.
- We were initially using a slightly incorrect set of intrinsics in ScanNet. The repo now uses intriniscs from the intriniscs folder.
- The MLP in the cost volume wasn't seeing any flip augmentation which led to biases around edges, so we've now included a geometry based flip in the base dataset class. It is enabled only for the train split.
- We had a bug in projection that never allowed the mask in the cost volume to properly function, so we've now switched to using the same normalization as in OpenCV and Kornia.
Thanks to all those that pointed it out and were patient while we worked on fixes.
All scores improve with these fixes, and the associated weights are uploaded here. For old scores, code, and weights, check this commit hash: 7de5b451e340f9a11c7fd67bd0c42204d0b009a9
Full scores for models with bug fixes:
_Depth_
| `--config` | Abs Diff↓ | Abs Rel↓ | Sq Rel↓ | RMSE↓ | log RMSE↓ |delta < 1.05↑ | delta < 1.10↑ |
|-------------|-----------|----------|---------|---------|-------------|--------------|---------------|
| `hero_model.yaml`, Metadata + Resnet | 0.0868 | 0.0428 | 0.0127 | 0.1472 | 0.0681 | 74.26 | 90.88 |
| `dot_product_model.yaml`, dot product + Resnet | 0.0910 | 0.0453 | 0.0134 | 0.1509 | 0.0704 | 71.90 | 89.75 |
_Mesh Fusion_
| `--config` | Acc↓ | Comp↓ | Chamfer↓ | Recall↑ | Precision↑ | F-Score↑ |
|-------------|------|-------|----------|---------|------------|----------|
| `hero_model.yaml`, Metadata + Resnet | 5.41 | 5.98 | 5.69 | 0.695 | 0.668 | 0.680 |
| `dot_product_model.yaml`, dot product + Resnet | 5.66 | 6.18 | 5.92 | 0.682 | 0.655 | 0.667 |
_Comparison:_
| `--config` | Model | Abs Diff↓| Sq Rel↓ | delta < 1.05↑| Chamfer↓ | F-Score↑ |
|-------------|----------|--------------------|---------|---------|--------------|----------|
| `hero_model.yaml` | Metadata + Resnet Matching | 0.0868 | 0.0127 | 74.26 | 5.69 | 0.680 |
| OLD `hero_model.yaml` | Metadata + Resnet Matching | 0.0885 | 0.0125 | 73.16 | 5.81 | 0.671 |
| `dot_product_model.yaml` | Dot Product + Resnet Matching | 0.0910 | 0.0134 | 71.90 | 5.92 | 0.667 |
| OLD `dot_product_model.yaml` | Dot Product + Resnet Matching | 0.0941 | 0.0139 | 70.48 | 6.29 | 0.642 |
### **Tiny bug with frame count:**
Initially this repo spat out tuple files for default DVMVS style keyframes with 9 extra frame of 25599 for the ScanNetv2 test set. There was a minor bug with handling lost tracking that's now fixed. This repo should now mimic the DVMVS keyframe buffer exactly, with 25590 keyframes for testing. The only effect this bug had was the inclusion of 9 extra frames, all the other tuples were exactly the same as that of DVMVS. The offending frames are in these scans
```
scan previous count new count
--------------------------------------
scene0711_00 393 392
scene0727_00 209 208
scene0736_00 1023 1022
scene0737_00 408 407
scene0751_00 165 164
scene0775_00 220 219
scene0791_00 227 226
scene0794_00 141 140
scene0795_00 102 101
```
The tuple files for default test have been updated. Since this is a small (~3e-4) difference in extra frames scored, the scores are unchanged.
## 🗺️💾 COLMAP Dataset
__TL;DR:__ Scale your poses and crop your images.
We do provide a dataloader for loading images from a COLMAP sparse reconstruction. For this to work with SimpleRecon, you'll need to crop your images to match the FOV of ScanNet (roughly similar to an iPhone's FOV in video mode), and scale your pose's location using known real world measurements. If these steps aren't taken, the cost volume won't be built correctly, and the network will not estimate depth properly.
## 🙏 Acknowledgements
We thank Aljaž Božič of [TransformerFusion](https://github.com/AljazBozic/TransformerFusion), Jiaming Sun of [Neural Recon](https://zju3dv.github.io/neuralrecon/), and Arda Düzçeker of [DeepVideoMVS](https://github.com/ardaduz/deep-video-mvs) for quickly providing useful information to help with baselines and for making their codebases readily available, especially on short notice.
The tuple generation scripts make heavy use of a modified version of DeepVideoMVS's [Keyframe buffer](https://github.com/ardaduz/deep-video-mvs/blob/master/dvmvs/keyframe_buffer.py) (thanks again Arda and co!).
The PyTorch point cloud fusion module at `torch_point_cloud_fusion` code is borrowed from 3DVNet's [repo](https://github.com/alexrich021/3dvnet/blob/main/mv3d/eval/pointcloudfusion_custom.py). Thanks Alexander Rich!
We'd also like to thank Niantic's infrastructure team for quick actions when we needed them. Thanks folks!
Mohamed is funded by a Microsoft Research PhD Scholarship (MRL 2018-085).
## 📜 BibTeX
If you find our work useful in your research please consider citing our paper:
```
@inproceedings{sayed2022simplerecon,
title={SimpleRecon: 3D Reconstruction Without 3D Convolutions},
author={Sayed, Mohamed and Gibson, John and Watson, Jamie and Prisacariu, Victor and Firman, Michael and Godard, Cl{\'e}ment},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
year={2022},
}
```
## 👩⚖️ License
Copyright © Niantic, Inc. 2022. Patent Pending.
All rights reserved.
Please see the [license file](LICENSE) for terms.
================================================
FILE: configs/data/7scenes_default.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/res_nas/shared/datasets/7scenes/
tuple_info_file_location: data_splits/7Scenes/dvmvs_split/
dataset_scan_split_file: data_splits/7Scenes/dvmvs_split/dvmvs_test_split.txt
dataset: 7scenes
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
frame_tuple_type: default
split: test
================================================
FILE: configs/data/7scenes_dense.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/res_nas/shared/datasets/7scenes/
tuple_info_file_location: data_splits/7Scenes/dvmvs_split/
dataset_scan_split_file: data_splits/7Scenes/dvmvs_split/dvmvs_test_split.txt
dataset: 7scenes
mv_tuple_file_suffix: _eight_view_deepvmvs_dense.txt
num_images_in_tuple: 8
frame_tuple_type: dense
split: test
================================================
FILE: configs/data/colmap_dense.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/res_nas/mohameds/datasets/colmap/
tuple_info_file_location: /mnt/res_nas/mohameds/datasets/colmap/tuples
dataset_scan_split_file: /mnt/res_nas/mohameds/datasets/colmap/test.txt
dataset: colmap
mv_tuple_file_suffix: _eight_view_deepvmvs_dense.txt
num_images_in_tuple: 8
frame_tuple_type: dense
split: test
================================================
FILE: configs/data/neucon_arkit_default.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/res_nas/mohameds/datasets/arkit
tuple_info_file_location: data_splits/arkit/
dataset_scan_split_file: data_splits/arkit/scans.txt
dataset: arkit
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
frame_tuple_type: default
split: test
================================================
FILE: configs/data/neucon_arkit_dense.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/res_nas/mohameds/datasets/arkit
tuple_info_file_location: data_splits/arkit/
dataset_scan_split_file: data_splits/arkit/scans.txt
dataset: arkit
mv_tuple_file_suffix: _eight_view_deepvmvs_dense.txt
num_images_in_tuple: 8
frame_tuple_type: dense
split: test
================================================
FILE: configs/data/scannet_default_test.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/scannet-data-png2/
tuple_info_file_location: data_splits/ScanNetv2/standard_split
dataset_scan_split_file: data_splits/ScanNetv2/standard_split/scannetv2_test.txt
dataset: scannet
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
# default means every keyframe defined by DVMVS.
frame_tuple_type: default
split: test
================================================
FILE: configs/data/scannet_default_train.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/scannet-data-png2/
tuple_info_file_location: data_splits/ScanNetv2/standard_split/
dataset_scan_split_file: data_splits/ScanNetv2/standard_split/scannetv2_train.txt
dataset: scannet
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
frame_tuple_type: default
split: train
================================================
FILE: configs/data/scannet_default_val.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/scannet-data-png2/
tuple_info_file_location: data_splits/ScanNetv2/standard_split/
dataset_scan_split_file: data_splits/ScanNetv2/standard_split/scannetv2_val.txt
dataset: scannet
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
frame_tuple_type: default
split: val
================================================
FILE: configs/data/scannet_dense_test.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/scannet-data-png2/
tuple_info_file_location: data_splits/ScanNetv2/standard_split/
dataset_scan_split_file: data_splits/ScanNetv2/standard_split/scannetv2_test.txt
dataset: scannet
mv_tuple_file_suffix: _eight_view_deepvmvs_dense.txt
num_images_in_tuple: 8
frame_tuple_type: dense
split: test
================================================
FILE: configs/data/scannet_dense_val.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/scannet-data-png2/
tuple_info_file_location: data_splits/ScanNetv2/standard_split/
dataset_scan_split_file: data_splits/ScanNetv2/standard_split/scannetv2_val.txt
dataset: scannet
mv_tuple_file_suffix: _eight_view_deepvmvs_dense.txt
num_images_in_tuple: 8
frame_tuple_type: dense
split: val
================================================
FILE: configs/data/scanniverse_dense.yaml
================================================
!!python/object:options.Options
dataset_path: /mnt/res_nas/mohameds/datasets/scanniverse/
tuple_info_file_location: /mnt/res_nas/mohameds/datasets/scanniverse/tuples
dataset_scan_split_file: /mnt/res_nas/mohameds/datasets/scanniverse/scans.txt
dataset: scanniverse
mv_tuple_file_suffix: _eight_view_deepvmvs_dense.txt
num_images_in_tuple: 8
frame_tuple_type: dense
split: test
================================================
FILE: configs/data/vdr_default.yaml
================================================
!!python/object:options.Options
dataset_path: /media/data/mosayed/datasets/vdr
tuple_info_file_location: data_splits/vdr/
dataset_scan_split_file: data_splits/vdr/scans.txt
dataset: vdr
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
frame_tuple_type: default
split: test
================================================
FILE: configs/data/vdr_dense.yaml
================================================
!!python/object:options.Options
dataset_path: /media/data/mosayed/datasets/vdr
tuple_info_file_location: data_splits/vdr/
dataset_scan_split_file: data_splits/vdr/scans.txt
dataset: vdr
mv_tuple_file_suffix: _eight_view_deepvmvs_dense.txt
num_images_in_tuple: 8
frame_tuple_type: dense
split: test
================================================
FILE: configs/data/vdr_dense_offline.yaml
================================================
!!python/object:options.Options
dataset_path: /media/data/mosayed/datasets/vdr
tuple_info_file_location: data_splits/vdr/
dataset_scan_split_file: data_splits/vdr/scans.txt
dataset: vdr
mv_tuple_file_suffix: _eight_view_deepvmvs_dense_offline.txt
num_images_in_tuple: 8
frame_tuple_type: dense_offline
split: test
================================================
FILE: configs/data/vdr_dense_rerun.yaml
================================================
!!python/object:options.Options
dataset_path: data/vdr
tuple_info_file_location: data_splits/vdr/
dataset_scan_split_file: data_splits/vdr/scans.txt
dataset: vdr
mv_tuple_file_suffix: _eight_view_deepvmvs_dense.txt
num_images_in_tuple: 8
frame_tuple_type: dense
split: test
================================================
FILE: configs/models/dot_product_model.yaml
================================================
!!python/object:options.Options
feature_volume_type: simple_cost_volume
batch_size: 16
cost_volume_aggregation: dot
cv_encoder_type: multi_scale_encoder
depth_decoder_name: unet_pp
gpus: 2
image_encoder_name: efficientnet
log_interval: 100
loss_type: log_l1
lr: 0.0001
wd: 0.0001
matching_encoder_type: resnet
name: dot_product_model
num_sanity_val_steps: 0
num_workers: 12
precision: 16
random_seed: 0
================================================
FILE: configs/models/hero_model.yaml
================================================
!!python/object:options.Options
feature_volume_type: mlp_feature_volume
batch_size: 16
cost_volume_aggregation: dot
cv_encoder_type: multi_scale_encoder
depth_decoder_name: unet_pp
gpus: 2
image_encoder_name: efficientnet
log_interval: 100
loss_type: log_l1
lr: 0.0001
wd: 0.0001
matching_encoder_type: resnet
name: hero_model
num_sanity_val_steps: 0
num_workers: 12
precision: 16
random_seed: 0
================================================
FILE: data_scripts/7scenes_preprocessing.py
================================================
# Code from https://github.com/tsattler/visloc_pseudo_gt_limitations/
import os
import warnings
import numpy as np
from skimage import io
from joblib import Parallel, delayed
# name of the folder where we download the original 7scenes dataset to
# we restructure the dataset by creating symbolic links to that folder
src_folder = '/mnt/res_nas/shared/datasets/7scenes'
focal_length = 525.0
# focal length of the depth sensor (source: https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)
d_focal_length = 585.0
# RGB image dimensions
img_w = 640
img_h = 480
# sub sampling factor of eye coordinate tensor
nn_subsampling = 8
#transformation from depth sensor to RGB sensor
#calibration according to https://projet.liris.cnrs.fr/voir/activities-dataset/kinect-calibration.html
d_to_rgb = np.array([
[ 9.9996518012567637e-01, 2.6765126468950343e-03, -7.9041012313000904e-03, -2.5558943178152542e-02],
[-2.7409311281316700e-03, 9.9996302803027592e-01, -8.1504520778013286e-03, 1.0109636268061706e-04],
[ 7.8819942130445332e-03, 8.1718328771890631e-03, 9.9993554558014031e-01, 2.0318321729487039e-03],
[0, 0, 0, 1]
])
def mkdir(directory):
"""Checks whether the directory exists and creates it if necessacy."""
if not os.path.exists(directory):
os.makedirs(directory)
# download the original 7 scenes dataset for poses and images
# mkdir(src_folder)
# os.chdir(src_folder)
# for ds in ['chess', 'fire', 'heads', 'office', 'pumpkin', 'redkitchen', 'stairs']:
# print("=== Downloading 7scenes Data:", ds, "===============================")
# os.system('wget http://download.microsoft.com/download/2/8/5/28564B23-0828-408F-8631-23B1EFF1DAC8/' + ds + '.zip')
# os.system('unzip ' + ds + '.zip')
# os.system('rm ' + ds + '.zip')
# sequences = os.listdir(ds)
# for file in sequences:
# if file.endswith('.zip'):
# print("Unpacking", file)
# os.system('unzip ' + ds + '/' + file + ' -d ' + ds)
# os.system('rm ' + ds + '/' + file)
# print("Processing frames...")
def process_scene(ds):
def process_frames(split_file):
# read the split file
with open(ds + '/' + split_file, 'r') as f:
split = f.readlines()
# map sequences to folder names
split = ['seq-' + s.strip()[8:].zfill(2) for s in split]
for seq in split:
files = os.listdir(ds + '/' + seq)
# adjust depth files by mapping to RGB sensor
depth_files = [f for f in files if f.endswith('depth.png')]
for d_index, d_file in enumerate(depth_files):
if d_index % 1000 == 0:
print(d_index, ds, split_file)
depth = io.imread(ds + '/' + seq + '/' + d_file)
depth = depth.astype(np.float32)
depth /= 1000 # from millimeters to meters
d_h = depth.shape[0]
d_w = depth.shape[1]
# reproject depth map to 3D eye coordinates
eye_coords = np.zeros((4, d_h, d_w))
# set x and y coordinates
eye_coords[0] = 0.5 + np.dstack([np.arange(0, d_w)] * d_h)[0].T
eye_coords[1] = 0.5 + np.dstack([np.arange(0, d_h)] * d_w)[0]
eye_coords = eye_coords.reshape(4, -1)
depth = depth.reshape(-1)
# filter pixels with invalid depth
depth_mask = (depth > 0) & (depth < 100)
eye_coords = eye_coords[:, depth_mask]
depth = depth[depth_mask]
# substract depth principal point (assume image center)
eye_coords[0] -= d_w / 2
eye_coords[1] -= d_h / 2
# reproject
eye_coords[0:2] /= d_focal_length
eye_coords[0] *= depth
eye_coords[1] *= depth
eye_coords[2] = depth
eye_coords[3] = 1
# transform from depth sensor to RGB sensor
eye_coords = np.matmul(d_to_rgb, eye_coords)
# project
depth = eye_coords[2]
eye_coords[0] /= depth
eye_coords[1] /= depth
eye_coords[0:2] *= focal_length
# add RGB principal point (assume image center)
eye_coords[0] += img_w / 2
eye_coords[1] += img_h / 2
registered_depth = np.ones((img_h, img_w), dtype=np.float32) * 2e3
for pt in range(eye_coords.shape[1]):
x = round(eye_coords[0, pt])
y = round(eye_coords[1, pt])
d = eye_coords[2, pt]
if x < 0 or y < 0 or y >= d_h or x >= d_w:
continue
registered_depth[y, x] = min(registered_depth[y, x], d)
registered_depth[registered_depth > 1e3] = 0
registered_depth = (1000 * registered_depth).astype(np.uint16)
# store calibrated depth
with warnings.catch_warnings():
warnings.simplefilter("ignore")
io.imsave(ds + '/' + seq + '/' + d_file.replace("depth.png", "depth.proj.png"), registered_depth)
process_frames('TrainSplit.txt')
process_frames('TestSplit.txt')
Parallel(n_jobs=7, verbose=0)(
map(delayed(process_scene), [os.path.join(src_folder, scan_name) for scan_name in ['chess', 'fire', 'heads', 'office', 'pumpkin', 'redkitchen', 'stairs']]))
================================================
FILE: data_scripts/IOS_LOGGER_ARKIT_README.md
================================================
# Running with NeuralRecon's demo data and data from ios-logger.
Download the demo scene out of ios-logger from here: https://github.com/zju3dv/NeuralRecon/blob/master/DEMO.md
Follow the instructions in the NeuralRecon repo for how to use ios-logger to make your own captures.
Unzip the folder into your arkit dataset path so that it looks somthing like this:
```
dataset_path
scans
neucon_demodata_b5f1
...
....
```
Run the extraction script that uses modified versions of the functions provided by the NeuralRecon authors:
```bash
python ./data_scripts/ios_logger_preprocessing.py --data_config configs/data/neucon_arkit_default.yaml
```
Make sure you set your correct `dataset_path` folder.
Run tuple file generation (we've already computed one for you in data_splits):
```bash
python ./data_scripts/generate_test_tuples.py --num_workers 16 --data_config configs/data/neucon_arkit_default.yaml
```
Then run the model using this config file, see the full readme for more.
There is unfortunately a break in the pose in the NR demo scene, so you'll to trim the first 350 frames using `--skip_to_frame 350` when running dense frames and `--skip_to_frame 83` when running default.
Run:
```bash
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/neucon_arkit_default.yaml \
--num_workers 8 \
--batch_size 2 \
--fast_cost_volume \
--run_fusion \
--depth_fuser open3d \
--fuse_color \
--skip_to_frame 83;
```
```bash
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/neucon_arkit_dense.yaml \
--num_workers 8 \
--batch_size 2 \
--fast_cost_volume \
--run_fusion \
--depth_fuser open3d \
--fuse_color \
--skip_to_frame 350;
```
Should get an output that looks like this for default frames:
================================================
FILE: data_scripts/generate_test_tuples.py
================================================
"""Script for generating DeeoVideoMVS multiview lists in the split folder
indicated. It will export these frame tuples in this format line by line in
the output file:
scan_id frame_id_0 frame_id_1 ... frame_id_N-1
where frame_id_0 is the reference image.
Run like so for generating a list of test tuples of eight frames (default):
python ./data_scripts/generate_test_tuples.py
--data_config configs/data/scannet_default_test.yaml
--num_workers 16
where scannet_default_test.yaml looks like:
!!python/object:options.Options
dataset_path: SCANNET_PATH/
tuple_info_file_location: $tuples_directory$
dataset_scan_split_file: $test_split_list_location$
dataset: scannet
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
frame_tuple_type: default
split: test
frame_tuple_type defines the type of test tuples to produce.
default: Used for all depth and fusion scores.
Will produce default online DVMVS style tuples (online) for each
keyframe. Online means all neighboring frames used in the tuple are in
the past.
offline: Like default but will produce an offline tuple for each keyframe.
Offline means that selected neighboring frames (source frames) will be
both in the future and in the past.
dense: Like default but will produce an online tuple for each frame in the
scan.
dense_offline: Like offline but will produce an offline tuple for each frame
in the scan.
It will output a tuples file with a tuple list at:
{tuple_info_file_location}/{split}{mv_split_filesuffix}
This file uses the defaults for keyframe distances from DVMVS.
"""
import os
import sys
sys.path.append("/".join(sys.path[0].split("/")[:-1]))
import random
import string
from functools import partial
from multiprocessing import Manager
from multiprocessing.pool import Pool
from pathlib import Path
import numpy as np
import options
import tools.keyframe_buffer
from tools.keyframe_buffer import DVMVS_Config
from utils.dataset_utils import get_dataset
def compute_offline_tuple(
poses,
n_measurement_frames,
current_keyframe_index,
reference_pose,
):
"""
Computes an offline tuple by scanning keyframes backwards and forwards in
time.
Args:
poses: poses for the scan
n_measurement_frames: number of measurement (source) frames required for
each tuple. Total number of frames in the tuple is
n_measurement_frames+1
current_keyframe_index: the current frame
reference_pose: the pose of the current frame at current_keyframe_index
Returns:
sample: a dictionary with the key indices whose values are the indices
of the tuple.
"""
sample = {'indices': [current_keyframe_index]}
tuple_keyframe_buffer = tools.keyframe_buffer.OfflineKeyframeBuffer(
buffer_size=DVMVS_Config.test_keyframe_buffer_size*2,
keyframe_pose_distance=DVMVS_Config.test_keyframe_pose_distance,
optimal_t_score=DVMVS_Config.test_optimal_t_measure,
optimal_R_score=DVMVS_Config.test_optimal_R_measure,
store_return_indices=True,
)
# prime the buffer with the reference pose
response = tuple_keyframe_buffer.try_new_keyframe(
reference_pose.copy(),
None,
index=current_keyframe_index,
)
current_backwards_index = current_keyframe_index-1
current_forwards_index = current_keyframe_index+1
direction = True
count_added = 0
exhausted_forward = False
exhausted_backward = False
while not exhausted_forward or not exhausted_backward:
# step away from the current frame alternating in both directions
# fill the buffer with keyframes from both direcitons as we go.
if exhausted_forward and exhausted_backward:
break
# check which way we should be looking
if direction:
# going forward, so get the forward pose to update the buffer
direction = False
if current_forwards_index >= len(poses):
exhausted_forward = True
continue
new_frame_index = current_forwards_index
new_frame_pose = poses[new_frame_index].copy()
current_forwards_index += 1
else:
# going backward, so get the forward pose to update the buffer
direction = True
if current_backwards_index < 0:
exhausted_backward = True
continue
new_frame_index = current_backwards_index
new_frame_pose = poses[new_frame_index].copy()
current_backwards_index -= 1
# poll the buffer,
response = tuple_keyframe_buffer.try_new_keyframe(
new_frame_pose, None, index=new_frame_index
)
if response == 1:
count_added +=1
# if we have enough frames, then stop.
if count_added >= DVMVS_Config.test_keyframe_buffer_size*2:
break
# the buffer is full, now get source frames.
measurement_frames = (tuple_keyframe_buffer.
get_best_measurement_frames_for_0index(n_measurement_frames))
for (_, _, measurement_index) in measurement_frames:
sample["indices"].append(measurement_index)
return sample
def default_dvmvs_tuples(scan, poses, dists_to_last_valid, n_measurement_frames):
"""
Creates a list of default DVMVS tuples for a scan's poses. Only tuples at
keyframes will be returned. Each tuple will be online (frames will be behind
the current frame in time.)
This will not return a tuple for the first frame.
Args:
scan: scan id string.
poses: a list of poses.
dists_to_last_valid: list with distances from each current valid frame
to the last valid frame. Pass Nones if you you're passing invalid
poses and want the DVMVS keyframe handler to take care of them.
n_measurement_frames: number of measurement (source) frames required for
each tuple. Total number of frames in the tuple is
n_measurement_frames+1
Returns:
samples: a list of dictionaries where each item dicitonary contians
a scan's id and a list of indices for the tuple's frames.
"""
keyframe_buffer = tools.keyframe_buffer.KeyframeBuffer(
buffer_size=DVMVS_Config.test_keyframe_buffer_size,
keyframe_pose_distance=DVMVS_Config.test_keyframe_pose_distance,
optimal_t_score=DVMVS_Config.test_optimal_t_measure,
optimal_R_score=DVMVS_Config.test_optimal_R_measure,
store_return_indices=True,
)
samples = []
for i in range(0, len(poses)):
sample = {'scan': scan,
'indices': [i]}
reference_pose = poses[i].copy()
# POLL THE KEYFRAME BUFFER
response = keyframe_buffer.try_new_keyframe(
reference_pose, None, dists_to_last_valid[i], index=i
)
if response == 3:
print("Tracking lost!")
elif response == 1:
measurement_frames = (keyframe_buffer.
get_best_measurement_frames(n_measurement_frames))
for (_, _, measurement_index) in measurement_frames:
sample["indices"].append(measurement_index)
samples.append(sample)
return samples
def offline_dvmvs_tuples(scan, poses, n_measurement_frames):
"""
Creates a list of offline DVMVS tuples for a scan's poses. Only tuples at
keyframes will be returned. This will provide frames both ahead of and
behind the current frame.
This will not return a tuple for the first frame.
Args:
scan: scan id string.
poses: a list of poses.
n_measurement_frames: number of measurement (source) frames required for
each tuple. Total number of frames in the tuple is
n_measurement_frames+1
Returns:
samples: a list of dictionaries where each item dicitonary contians
a scan's id and a list of indices for the tuple's frames.
"""
samples = []
keyframe_buffer = tools.keyframe_buffer.KeyframeBuffer(
buffer_size=DVMVS_Config.test_keyframe_buffer_size,
keyframe_pose_distance=DVMVS_Config.test_keyframe_pose_distance,
optimal_t_score=DVMVS_Config.test_optimal_t_measure,
optimal_R_score=DVMVS_Config.test_optimal_R_measure,
store_return_indices=True,
)
for i in range(0, len(poses)):
reference_pose = poses[i].copy()
response = keyframe_buffer.try_new_keyframe(
pose=reference_pose.copy(),
image=None,
index=i
)
if response != 1:
continue
sample = compute_offline_tuple(poses, n_measurement_frames,
i, reference_pose.copy())
sample['scan'] = scan
if len(sample["indices"]) == 1 and i == 0:
# first frame and no indices available.
continue
else:
samples.append(sample)
return samples
def dense_dvmvs_tuples(scan, poses, n_measurement_frames):
"""
Creates a list of DVMVS online tuples for a scan's poses at each frame.
This will not return a tuple for the first frame.
Args:
scan: scan id string.
poses: a list of poses.
n_measurement_frames: number of measurement (source) frames required for
each tuple. Total number of frames in the tuple is
n_measurement_frames+1
Returns:
samples: a list of dictionaries where each item dicitonary contians
a scan's id and a list of indices for the tuple's frames.
"""
samples = []
for i in range(0, len(poses)):
sample = {'scan': scan,
'indices': [i]}
reference_pose = poses[i]
keyframe_buffer = tools.keyframe_buffer.OfflineKeyframeBuffer(
buffer_size=DVMVS_Config.test_keyframe_buffer_size,
keyframe_pose_distance=DVMVS_Config.test_keyframe_pose_distance,
optimal_t_score=DVMVS_Config.test_optimal_t_measure,
optimal_R_score=DVMVS_Config.test_optimal_R_measure,
store_return_indices=True)
response = keyframe_buffer.try_new_keyframe(
reference_pose, None, index=i
)
num_available_measurement_frames = 0
current_backwards_index = i-1
exhausted_backward = False
count_added = 0
while num_available_measurement_frames != n_measurement_frames:
# POLL THE KEYFRAME BUFFER
if exhausted_backward:
break
if current_backwards_index < 0:
exhausted_backward = True
break
new_frame_index = current_backwards_index
new_frame_pose = poses[new_frame_index]
current_backwards_index -= 1
response = keyframe_buffer.try_new_keyframe(
new_frame_pose, None, index=new_frame_index
)
if response == 1:
count_added +=1
if count_added >= DVMVS_Config.test_keyframe_buffer_size:
break
measurement_frames = (keyframe_buffer.
get_best_measurement_frames_for_0index(n_measurement_frames))
for (_, _, measurement_index) in measurement_frames:
sample["indices"].append(measurement_index)
if len(sample["indices"]) == 1 and i == 0:
# first frame and no indices available.
continue
else:
samples.append(sample)
return samples
def offline_dense_dvmvs_tuples(scan, poses, n_measurement_frames):
"""
Creates a list of DVMVS offline tuples for a scan's poses at each frame.
This will not return a tuple for the first frame. For frames where there are
not enough keyframes to build the tuple (near the beginning of the sequency)
frame indices will be repeated to compensate and pad the tuple.
Args:
scan: scan id string.
poses: a list of poses.
n_measurement_frames: number of measurement (source) frames required for
each tuple. Total number of frames in the tuple is
n_measurement_frames+1
Returns:
samples: a list of dictionaries where each item dicitonary contians
a scan's id and a list of indices for the tuple's frames.
"""
samples = []
for i in range(0, len(poses)):
sample = {'scan': scan,
'indices': [i]}
reference_pose = poses[i]
sample = compute_offline_tuple(poses, n_measurement_frames,
i, reference_pose)
sample['scan'] = scan
if len(sample["indices"]) == 1 and i == 0:
# first frame and no indices available.
continue
else:
samples.append(sample)
return samples
def crawl_subprocess_long(opts_temp_filepath, scan, count, progress):
"""
Returns a list of tuples according to the options at opts_temp_filepath.
This will not return a tuple for the first frame. For frames where there are
not enough keyframes to build the tuple (near the beginning of the sequency)
frame indices will be repeated to compensate and pad the tuple.
Args:
opts_temp_filepath: filepath for an options config file.
scan: scan to operate on.
count: total count of multi process scans.
progress: a Pool() progress value for tracking progress. For debugging
you can pass
multiprocessing.Manager().Value('i', 0)
for this.
Returns:
item_list: a list of strings where each string is the cocnatenated
scan id and frame_ids for every tuple.
scan_id frame_id_0 frame_id_1 ... frame_id_N-1
"""
item_list = []
# load options file
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options(
config_filepaths=opts_temp_filepath,
ignore_cl_args=True,
)
opts = option_handler.options
# get dataset
dataset_class, _ = get_dataset(
opts.dataset,
opts.dataset_scan_split_file,
opts.single_debug_scan_id,
verbose=False,
)
ds = dataset_class(
dataset_path=opts.dataset_path,
mv_tuple_file_suffix=None,
split=opts.split,
tuple_info_file_location=opts.tuple_info_file_location,
pass_frame_id=True,
verbose_init=False,
)
valid_frames = ds.get_valid_frame_ids(opts.split, scan)
try:
check_valid_dist = int(valid_frames[0].strip().split(" ")[2])
except:
if opts.frame_tuple_type == "default":
print(f"\nWARNING: Couldn't find max valid distances in the valid_frames "
f"file for scan {scan}. Please delete existing valid_frames.txt "
f"and rerun to regenerate valid frames. "
f"There's a difference of 9 extra frames out of 25599 for the "
f"ScanNetv2 test set.\n")
dists_to_last_valid = []
frame_ind_to_frame_id = {}
for frame_ind, frame_line in enumerate(valid_frames):
frame_ind_to_frame_id[frame_ind] = frame_line.strip().split(" ")[1]
try:
dists_to_last_valid.append(int(frame_line.strip().split(" ")[2]))
except:
# just add Noness
dists_to_last_valid.append(None)
poses = []
for frame_ind in range(len(valid_frames)):
frame_id = frame_ind_to_frame_id[frame_ind]
world_T_cam_44, _ = ds.load_pose(scan.rstrip("\n"), frame_id)
poses.append(world_T_cam_44)
subsequence_length = opts.num_images_in_tuple
n_measurement_frames = subsequence_length - 1
if opts.frame_tuple_type == "default":
samples = default_dvmvs_tuples(scan, poses, dists_to_last_valid, n_measurement_frames)
elif opts.frame_tuple_type == "offline":
samples = offline_dvmvs_tuples(scan, poses, n_measurement_frames)
elif opts.frame_tuple_type == "dense":
samples = dense_dvmvs_tuples(scan, poses, n_measurement_frames)
elif opts.frame_tuple_type == "dense_offline":
samples = offline_dense_dvmvs_tuples(scan, poses, n_measurement_frames)
else:
ValueError(f"Not a recognized tuple frame type: "
f"{opts.frame_tuple_type}")
num_repeats = 0
for sample in samples:
sampled_indices = sample["indices"]
if len(sampled_indices) != subsequence_length:
# not enough frames in the buffer.
# get all frames seen so far that aren't keyframes
available_indices = list(range(0, sampled_indices[0]))
available_indices = [frame_ind for frame_ind in available_indices
if frame_ind not in sampled_indices]
# pick from frames that haven't been touched yet (available_indices).
diff = subsequence_length - len(sampled_indices)
if diff > len(available_indices):
diff = len(available_indices)
# preferably pick from recent frames
back_search_dist = (30 if len(available_indices) >= 30 else
len(available_indices))
sampled_indices += random.sample(
available_indices[-back_search_dist:],
k=diff
)
# check again in case we still don't have enough and random sample
# repeat if we don't
if len(sampled_indices) != subsequence_length:
diff = subsequence_length - len(sampled_indices)
num_repeats+=diff
sampled_indices += random.choices(sampled_indices[1:], k=diff)
assert len(sampled_indices) == subsequence_length
chosen_frame_ids = [frame_ind_to_frame_id[frame_ind]
for frame_ind in sampled_indices]
cat_ids = ' '.join([str(frame_id) for frame_id in chosen_frame_ids])
item_list.append(f"{scan} {cat_ids}")
progress.value += 1
print(f"Completed scan {scan}, {progress.value} of total {count}. "
f"# Samples: {len(samples)} of {len(poses)} poses. "
f"Samples with repeated frames: {num_repeats}.\r")
return item_list
def crawl(opts_temp_filepath, opts, scans):
"""
Multiprocessing helper for crawl_subprocess_long.
Returns a list of tuples according to the options at opts_temp_filepath.
Args:
opts_temp_filepath: filepath for an options config file.
opts: options dataclass.
scans: scans to multiprocess.
Returns:
item_list: a list of strings where each string is the cocnatenated
scan id and frame_ids for every tuple for every scan in scans.
scan_id frame_id_0 frame_id_1 ... frame_id_N-1
"""
pool = Pool(opts.num_workers)
manager = Manager()
count = len(scans)
progress = manager.Value('i', 0)
item_list = []
for scan_item_list in pool.imap_unordered(
partial(
crawl_subprocess_long,
opts_temp_filepath,
count=count,
progress=progress
),
scans,
):
item_list.extend(scan_item_list)
return item_list
if __name__ == '__main__':
# load options file
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options(ignore_cl_args=False)
option_handler.pretty_print_options()
opts = option_handler.options
Path(os.path.join(os.path.expanduser("~"), "tmp/")).mkdir(
parents=True, exist_ok=True
)
opts_temp_filepath = os.path.join(
os.path.expanduser("~"),
"tmp/",
''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+ ".yaml",
)
option_handler.save_options_as_yaml(opts_temp_filepath, opts)
np.random.seed(42)
random.seed(42)
if opts.gpus == 0:
print("Setting precision to 32 bits since --gpus is set to 0.")
opts.precision = 32
# get dataset
dataset_class, scan_names = get_dataset(
opts.dataset,
opts.dataset_scan_split_file,
opts.single_debug_scan_id,
)
item_list = []
Path(opts.tuple_info_file_location).mkdir(exist_ok=True, parents=True)
split_filename = f"{opts.split}{opts.mv_tuple_file_suffix}"
split_filepath = os.path.join(opts.tuple_info_file_location, split_filename)
print(f"Saving to {split_filepath}\n")
if opts.single_debug_scan_id is not None:
item_list = crawl_subprocess_long(
opts_temp_filepath,
opts.single_debug_scan_id,
0, Manager().Value('i', 0),
)
else:
item_list = crawl(opts_temp_filepath, opts, scan_names)
with open(split_filepath, "w") as f:
for line in item_list:
f.write(line + "\n")
print(f"Saved to {split_filepath}")
================================================
FILE: data_scripts/generate_train_tuples.py
================================================
"""Script for generating DeeoVideoMVS multiview lists in the split folder
indicated. It will export these frame tuples in this format line by line in
the output file:
scan_id frame_id_0 frame_id_1 ... frame_id_N-1
where frame_id_0 is the reference image.
Run like so for generating a list of train tuples of eight frames (default):
python ./data_scripts/generate_train_tuples.py
--data_config configs/data/scannet_default_train.yaml
--num_workers 16
where scannet_default_train.yaml looks like:
!!python/object:options.Options
dataset_path: SCANNET_PATH/
tuple_info_file_location: $tuples_directory$
dataset_scan_split_file: $train_split_list_location$
dataset: scannet
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
frame_tuple_type: default
split: train
For val, use configs/data/scannet_default_val.yaml.
It will output a tuples file with a tuple list at:
{tuple_info_file_location}/{split}{mv_split_filesuffix}
This file uses the defaults for frame distances from DVMVS.
This module also borrows the main tuple generation function from
the DeepVideoMVS repo https://github.com/ardaduz/deep-video-mvs
"""
import copy
import os
import sys
sys.path.append("/".join(sys.path[0].split("/")[:-1]))
import random
import string
from functools import partial
from multiprocessing import Manager
from multiprocessing.pool import Pool
from pathlib import Path
import numpy as np
import options
from tools.keyframe_buffer import DVMVS_Config, is_valid_pair
from utils.dataset_utils import get_dataset
def gather_pairs_train(poses, used_pairs,
is_backward, initial_pose_dist_min, initial_pose_dist_max):
sequence_length = len(poses)
while_range = range(0, sequence_length)
pose_dist_min = copy.deepcopy(initial_pose_dist_min)
pose_dist_max = copy.deepcopy(initial_pose_dist_max)
used_measurement_indices = set()
# Gather pairs
check_future = False
pairs = []
if is_backward:
i = sequence_length - 1
step = -1
first_limit = 5
second_limit = sequence_length - 5
else:
i = 0
step = 1
first_limit = sequence_length - 5
second_limit = 5
loosening_counter = 0
while i in while_range:
pair = (i, -1)
if check_future:
for j in range(i + step, first_limit, step):
if (j not in used_measurement_indices
and (i, j) not in used_pairs):
valid = is_valid_pair(poses[i], poses[j],
pose_dist_min, pose_dist_max)
if valid:
pair = (i, j)
pairs.append(pair)
used_pairs.add(pair)
used_pairs.add((pair[1], pair[0]))
used_measurement_indices.add(j)
pose_dist_min = copy.deepcopy(initial_pose_dist_min)
pose_dist_max = copy.deepcopy(initial_pose_dist_max)
i += step
check_future = False
loosening_counter = 0
break
else:
for j in range(i - step, second_limit, -step):
if j not in used_measurement_indices and (i, j) not in used_pairs:
valid = is_valid_pair(poses[i], poses[j],
pose_dist_min, pose_dist_max)
if valid:
pair = (i, j)
pairs.append(pair)
used_pairs.add(pair)
used_pairs.add((pair[1], pair[0]))
used_measurement_indices.add(j)
pose_dist_min = copy.deepcopy(initial_pose_dist_min)
pose_dist_max = copy.deepcopy(initial_pose_dist_max)
i += step
check_future = False
loosening_counter = 0
break
if pair[1] == -1:
if check_future:
pose_dist_min = pose_dist_min / 1.1
pose_dist_max = pose_dist_max * 1.1
check_future = False
loosening_counter += 1
if loosening_counter > 1:
i += step
loosening_counter = 0
else:
check_future = True
else:
check_future = False
return pairs
def crawl_subprocess_short(opts_temp_filepath, scan, count, progress):
"""
Returns a list of DVMVS train tuples according to the options at
opts_temp_filepath for two frame tuples.
Args:
opts_temp_filepath: filepath for an options config file.
scan: scan to operate on.
count: total count of multi process scans.
progress: a Pool() progress value for tracking progress. For debugging
you can pass
multiprocessing.Manager().Value('i', 0)
for this.
Returns:
item_list: a list of strings where each string is the cocnatenated
scan id and frame_ids for every tuple.
scan_id frame_id_0 frame_id_1 ... frame_id_N-1
"""
scan_item_list = []
# load options file
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options(config_filepaths=opts_temp_filepath,
ignore_cl_args=True)
opts = option_handler.options
# get dataset
dataset_class, _ = get_dataset(opts.dataset,
opts.dataset_scan_split_file,
opts.single_debug_scan_id, verbose=False)
ds = dataset_class(dataset_path=opts.dataset_path,
mv_tuple_file_suffix=None,
split=opts.split,
tuple_info_file_location=opts.tuple_info_file_location,
pass_frame_id=True,
verbose_init=False)
valid_frames = ds.get_valid_frame_ids(opts.split, scan)
frame_ind_to_frame_id = {}
for frame_ind, frame_line in enumerate(valid_frames):
frame_ind_to_frame_id[frame_ind] = frame_line.strip().split(" ")[1]
poses = []
for frame_ind in range(len(valid_frames)):
frame_id = frame_ind_to_frame_id[frame_ind]
world_T_cam_44, _ = ds.load_pose(scan.rstrip("\n"), frame_id)
poses.append(world_T_cam_44)
samples = []
used_pairs = set()
for multiplier in [(1.0, False), (0.666, True), (1.5, False)]:
pairs = gather_pairs_train(poses, used_pairs,
is_backward=multiplier[1],
initial_pose_dist_min=(multiplier[0] *
DVMVS_Config.train_minimum_pose_distance),
initial_pose_dist_max=(multiplier[0] *
DVMVS_Config.train_maximum_pose_distance))
for pair in pairs:
i, j = pair
sample = {'scan': scan,
'indices': [i, j]}
samples.append(sample)
for sample in samples:
chosen_frame_ids = [ds.idx_to_scan_id_frame_id(frame_ind)[1]
for frame_ind in sample["indices"]]
cat_ids = ' '.join([str(frame_id) for frame_id in chosen_frame_ids])
scan_item_list.append(f"{scan} {cat_ids}")
progress.value += 1
print(f"Completed scan {scan}, {progress.value} of total {count}\r")
return scan_item_list
def crawl_subprocess_long(opts_temp_filepath, scan, count, progress):
"""
Returns a list of DVMVS train tuples according to the options at
opts_temp_filepath for tuples longer than two frames.
Args:
opts_temp_filepath: filepath for an options config file.
scan: scan to operate on.
count: total count of multi process scans.
progress: a Pool() progress value for tracking progress. For debugging
you can pass
multiprocessing.Manager().Value('i', 0)
for this.
Returns:
item_list: a list of strings where each string is the cocnatenated
scan id and frame_ids for every tuple.
scan_id frame_id_0 frame_id_1 ... frame_id_N-1
"""
scan_item_list = []
# load options file
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options(
config_filepaths=opts_temp_filepath,
ignore_cl_args=True,
)
opts = option_handler.options
# get dataset
dataset_class, _ = get_dataset(
opts.dataset,
opts.dataset_scan_split_file,
opts.single_debug_scan_id,
verbose=False,
)
ds = dataset_class(
dataset_path=opts.dataset_path,
mv_tuple_file_suffix=None,
split=opts.split,
tuple_info_file_location=opts.tuple_info_file_location,
pass_frame_id=True,
verbose_init=False,
)
valid_frames = ds.get_valid_frame_ids(opts.split, scan)
frame_ind_to_frame_id = {}
for frame_ind, frame_line in enumerate(valid_frames):
frame_ind_to_frame_id[frame_ind] = frame_line.strip().split(" ")[1]
poses = []
for frame_ind in range(len(valid_frames)):
frame_id = frame_ind_to_frame_id[frame_ind]
world_T_cam_44, _ = ds.load_pose(scan.rstrip("\n"), frame_id)
poses.append(world_T_cam_44)
subsequence_length = opts.num_images_in_tuple
sequence_length = len(poses)
used_pairs = set()
usage_threshold = 1
used_nodes = dict()
for i in range(sequence_length):
used_nodes[i] = 0
calculated_step = DVMVS_Config.train_crawl_step
samples = []
for offset, multiplier, is_backward in [(0 % calculated_step, 1.0, False),
(1 % calculated_step, 0.666, True),
(2 % calculated_step, 1.5, False),
(3 % calculated_step, 0.8, True),
(4 % calculated_step, 1.25, False),
(5 % calculated_step, 1.0, True),
(6 % calculated_step, 0.666, False),
(7 % calculated_step, 1.5, True),
(8 % calculated_step, 0.8, False),
(9 % calculated_step, 1.25, True)]:
if is_backward:
start = sequence_length - 1 - offset
step = -calculated_step
limit = subsequence_length
else:
start = offset
step = calculated_step
limit = sequence_length - subsequence_length + 1
for i in range(start, limit, step):
if used_nodes[i] > usage_threshold:
continue
sample = {'scan': scan,
'indices': [i]}
previous_index = i
valid_counter = 1
any_counter = 1
reached_sequence_limit = False
while valid_counter < subsequence_length:
if is_backward:
j = i - any_counter
reached_sequence_limit = j < 0
else:
j = i + any_counter
reached_sequence_limit = j >= sequence_length
if not reached_sequence_limit:
current_index = j
check1 = used_nodes[current_index] <= usage_threshold
check2 = (previous_index, current_index) not in used_pairs
check3 = is_valid_pair(
poses[previous_index],
poses[current_index],
(multiplier *
DVMVS_Config.train_minimum_pose_distance),
(multiplier *
DVMVS_Config.train_maximum_pose_distance),
t_norm_threshold=(multiplier *
DVMVS_Config.train_minimum_pose_distance * 0.5))
if check1 and check2 and check3:
sample['indices'].append(current_index)
previous_index = copy.deepcopy(current_index)
valid_counter += 1
any_counter += 1
else:
break
if not reached_sequence_limit:
previous_node = sample['indices'][0]
used_nodes[previous_node] += 1
for current_node in sample['indices'][1:]:
used_nodes[current_node] += 1
used_pairs.add((previous_node, current_node))
used_pairs.add((current_node, previous_node))
previous_node = copy.deepcopy(current_node)
samples.append(sample)
for sample in samples:
chosen_frame_ids = [frame_ind_to_frame_id[frame_ind] for
frame_ind in sample["indices"]]
cat_ids = ' '.join([str(frame_id) for frame_id in chosen_frame_ids])
scan_item_list.append(f"{scan} {cat_ids}")
progress.value += 1
print(f"Completed scan {scan}, {progress.value} of total {count}\r")
return scan_item_list
def crawl(opts_temp_filepath, opts, scans):
"""
Multiprocessing helper for crawl_subprocess_long and crawl_subprocess_long.
Returns a list of train tuples according to the options at
opts_temp_filepath.
Args:
opts_temp_filepath: filepath for an options config file.
opts: options dataclass.
scans: scans to multiprocess.
Returns:
item_list: a list of strings where each string is the cocnatenated
scan id and frame_ids for every tuple for every scan in scans.
scan_id frame_id_0 frame_id_1 ... frame_id_N-1
"""
pool = Pool(opts.num_workers)
manager = Manager()
count = len(scans)
progress = manager.Value('i', 0)
item_list = []
crawler = (crawl_subprocess_short if opts.num_images_in_tuple == 2
else crawl_subprocess_long)
for scan_item_list in pool.imap_unordered(
partial(
crawler,
opts_temp_filepath,
count=count,
progress=progress),
scans,
):
item_list.extend(scan_item_list)
return item_list
if __name__ == '__main__':
# load options file
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options(ignore_cl_args=False)
option_handler.pretty_print_options()
opts = option_handler.options
Path(os.path.join(os.path.expanduser("~"), "tmp/")).mkdir(
parents=True, exist_ok=True
)
opts_temp_filepath = os.path.join(
os.path.expanduser("~"),
"tmp/",
''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+ ".yaml",
)
option_handler.save_options_as_yaml(opts_temp_filepath, opts)
np.random.seed(42)
random.seed(42)
if opts.gpus == 0:
print("Setting precision to 32 bits since --gpus is set to 0.")
opts.precision = 32
# get dataset
dataset_class, scan_names = get_dataset(opts.dataset,
opts.dataset_scan_split_file,
opts.single_debug_scan_id, verbose=False)
Path(opts.tuple_info_file_location).mkdir(exist_ok=True, parents=True)
split_filename = f"{opts.split}{opts.mv_tuple_file_suffix}"
split_filepath = os.path.join(opts.tuple_info_file_location, split_filename)
print(f"Saving to {split_filepath}")
item_list = []
if opts.single_debug_scan_id is not None:
crawler = (crawl_subprocess_short if opts.num_images_in_tuple == 2
else crawl_subprocess_long)
item_list = crawler(
opts_temp_filepath,
opts.single_debug_scan_id,
0, Manager().Value('i', 0),
)
else:
item_list = crawl(opts_temp_filepath, opts, scan_names)
random.shuffle(item_list)
with open(split_filepath, "w") as f:
for line in item_list:
f.write(line + "\n")
print(f"Saved to {split_filepath}")
================================================
FILE: data_scripts/ios_logger_preprocessing.py
================================================
import os
import sys
sys.path.append("/".join(sys.path[0].split("/")[:-1]))
from datasets.arkit_dataset import process_data
import options
""" Download scans and extract each to a folder with their name like so:
dataset_path
scans
neucon_demodata_b5f1
...
....
"""
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options()
opts = option_handler.options
if opts.dataset_scan_split_file is not None:
f = open(opts.dataset_scan_split_file, "r")
scans = f.readlines()
scans = [scan.strip() for scan in scans]
f.close()
elif opts.single_debug_scan_id is not None:
scans = [opts.single_debug_scan_id]
else:
print("No valid scans pointers.")
for scan in scans:
path_dir = os.path.join(opts.dataset_path, "scans", scan)
process_data(path_dir)
================================================
FILE: data_scripts/precompute_valid_frames.py
================================================
"""Script for precomputing and storing a list of valid frames per scan. A valid
frame is defined as one that has an existing RGB frame, an existing depth
map, and a valid pose.
Run like so for test (default):
python ./scripts/precompute_valid_frames.py
--data_config configs/data/scannet_default_test.yaml
--num_workers 16
--split test
where scannet_default_test.yaml looks like:
!!python/object:options.Options
dataset_path: SCANNET_PATH/
tuple_info_file_location: SCANNET_PATH/tuples
dataset_scan_split_file: SCANNET_PATH/scannetv2_test.txt
dataset: scannet
mv_tuple_file_suffix: _eight_view_deepvmvs.txt
num_images_in_tuple: 8
frame_tuple_type: default
For validation, use scannet_default_val.yaml, and for train use
scannet_default_train.yaml
It will save a valid_frames.txt file where the dataset class defines.
"""
import os
import sys
sys.path.append("/".join(sys.path[0].split("/")[:-1]))
import random
import string
from functools import partial
from multiprocessing import Manager
from multiprocessing.pool import Pool
from pathlib import Path
import numpy as np
import options
from utils.dataset_utils import get_dataset
def process_scan(opts_temp_filepath, scan, count, progress):
"""
Precomputes a scan's valid frames by calling the dataset's appropriate
function.
Args:
opts_temp_filepath: filepath for an options config file.
scan: scan to operate on.
count: total count of multi process scans.
progress: a Pool() progress value for tracking progress. For debugging
you can pass
multiprocessing.Manager().Value('i', 0)
for this.
"""
item_list = []
# load options file
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options(config_filepaths=opts_temp_filepath,
ignore_cl_args=True)
opts = option_handler.options
# get dataset
dataset_class, _ = get_dataset(
opts.dataset,
opts.dataset_scan_split_file,
opts.single_debug_scan_id,
verbose=False,
)
ds = dataset_class(
dataset_path=opts.dataset_path,
mv_tuple_file_suffix=None,
split=opts.split,
tuple_info_file_location=opts.tuple_info_file_location,
pass_frame_id=True,
verbose_init=False,
)
_ = ds.get_valid_frame_ids(opts.split, scan)
progress.value += 1
print(f"Completed scan {scan}, {progress.value} of total {count}.")
return item_list
def multi_process_scans(opts_temp_filepath, opts, scans):
"""
Multiprocessing helper for crawl_subprocess_long and crawl_subprocess_long.
Precomputes a scan's valid frames by calling the dataset's appropriate
function.
Args:
opts_temp_filepath: filepath for an options config file.
opts: options dataclass.
scans: scans to multiprocess.
"""
pool = Pool(opts.num_workers)
manager = Manager()
count = len(scans)
progress = manager.Value('i', 0)
item_list = []
for scan_item_list in pool.imap_unordered(
partial(
process_scan,
opts_temp_filepath,
count=count,
progress=progress
),
scans,
):
item_list.extend(scan_item_list)
return item_list
if __name__ == '__main__':
# load options file
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options(ignore_cl_args=False)
option_handler.pretty_print_options()
opts = option_handler.options
opts_temp_filepath = os.path.join(
os.path.expanduser("~"),
"tmp/",
''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+ ".yaml",
)
option_handler.save_options_as_yaml(opts_temp_filepath, opts)
np.random.seed(42)
random.seed(42)
if opts.gpus == 0:
print("Setting precision to 32 bits since --gpus is set to 0.")
opts.precision = 32
# get dataset
dataset_class, scan_names = get_dataset(
opts.dataset,
opts.dataset_scan_split_file,
opts.single_debug_scan_id,
)
item_list = []
Path(opts.tuple_info_file_location).mkdir(exist_ok=True, parents=True)
split_filename = f"{opts.split}{opts.mv_tuple_file_suffix}"
split_filepath = os.path.join(opts.tuple_info_file_location, split_filename)
print(f"Processing valid frames.\n")
if opts.single_debug_scan_id is not None:
item_list = process_scan(
opts_temp_filepath,
opts.single_debug_scan_id,
0,
Manager().Value('i', 0),
)
else:
item_list = multi_process_scans(opts_temp_filepath, opts, scan_names)
with open(split_filepath, "w") as f:
for line in item_list:
f.write(line + "\n")
print(f"Complete")
================================================
FILE: data_scripts/scannet_wrangling_scripts/LICENSE
================================================
The LICENSE applies only to reader.py and SensorData.py.
Copyright 2017
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Niessner
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
================================================
FILE: data_scripts/scannet_wrangling_scripts/README.md
================================================
# Downloading and Extracting ScanNetv2
Developed and tested with python 3.9.
The included license at LICENSE applies only to `reader.py` and `SensorData.py`.
These scripts should help you export ScanNetv2 to the following format:
SCANNET_ROOT
scans_test (test scans)
scene0707
scene0707_00_vh_clean_2.ply (gt mesh)
sensor_data
frame-000261.pose.txt
frame-000261.color.jpg
frame-000261.color.512.png (optional, image at 512x384)
frame-000261.color.640.png (optional, image at 640x480)
frame-000261.depth.png (full res depth, stored scale *1000)
frame-000261.depth.256.png (optional, depth at 256x192 also
scaled)
scene0707.txt (scan metadata and image sizes)
intrinsic
intrinsic_depth.txt
intrinsic_color.txt
...
scans (val and train scans)
scene0000_00
(see above)
scene0000_01
....
Make sure all the packages in `env.yml` are installed in your environment.
## Downloading ScanNetv2
The `download_scannet.py` script is from https://kaldir.vc.in.tum.de/scannet/download-scannet.py
Please make sure you fill in this form before downloading the data:
https://kaldir.vc.in.tum.de/scannet/ScanNet_TOS.pdf
Download the dataset by running:
```
python download_scannet.py -o SCANNET_ROOT
```
For one scan debug use:
```
python download_scannet.py -o SCANNET_ROOT --id scene0707_00
```
This will download a `.sens` file, `.txt` file, the high resolution mesh `ply`, and a lower resolution mesh `ply`.
`.txt` will include meta information for the scan. See the next section for extracting the `.sens` file.
## Extracting data from .sens files
Please use the intrinsics directly from the downloaded `.txt` file from the dataset.
This is a modified version of the SensReader python script at
https://github.com/ScanNet/ScanNet/tree/master/SensReader/python
`reader.py` will extract depth, jpg, and intrinics files from ScanNetv2's downloaded `.sens` files. It will dump the `jpg` data directly to disk without uncompressing/compressing.
To extract all scans for test:
```
python reader.py --scans_folder SCANNET_ROOT/scans_test \
--output_path OUTPUT_PATH/scans_test \
--scan_list_file splits/scannetv2_test.txt \
--num_workers 12 \
--export_poses \
--export_depth_images \
--export_color_images \
--export_intrinsics;
```
For train and val
```
python reader.py --scans_folder SCANNET_ROOT/scans \
--output_path OUTPUT_PATH/scans \
--scan_list_file splits/scannetv2_train.txt \
--num_workers 12 \
--export_poses \
--export_depth_images \
--export_color_images \
--export_intrinsics;
python reader.py --scans_folder SCANNET_ROOT/scans \
--output_path OUTPUT_PATH/scans \
--scan_list_file splits/scannetv2_val.txt \
--num_workers 12 \
--export_poses \
--export_depth_images \
--export_color_images \
--export_intrinsics;
```
`OUTPUT_PATH` can be the same directory as the ScanNet root directory `SCANNET_ROOT`.
For one scan use `--single_debug_scan_id`.
For caching resized pngs for depth and color files, run:
```
python reader.py --scans_folder SCANNET_ROOT/scans \
--output_path OUTPUT_PATH/scans \
--scan_list_file splits/scannetv2_train.txt \
--num_workers 12 \
--export_depth_images \
--export_color_images \
--rgb_resize 512 384 \
--depth_resize 256 192;
```
and for images at `640x480`:
```
python reader.py --scans_folder SCANNET_ROOT/scans \
--output_path OUTPUT_PATH/scans \
--scan_list_file splits/scannetv2_train.txt \
--num_workers 12 \
--export_color_images \
--rgb_resize 640 480 \
```
================================================
FILE: data_scripts/scannet_wrangling_scripts/SensorData.py
================================================
import os, struct
import numpy as np
import zlib
import imageio
import cv2
import png
from PIL import Image
from contextlib import contextmanager
COMPRESSION_TYPE_COLOR = {-1:'unknown', 0:'raw', 1:'png', 2:'jpeg'}
COMPRESSION_TYPE_DEPTH = {-1:'unknown', 0:'raw_ushort', 1:'zlib_ushort', 2:'occi_ushort'}
@contextmanager
def print_array_on_one_line():
oldoptions = np.get_printoptions()
np.set_printoptions(linewidth=np.inf)
np.set_printoptions(linewidth=np.inf)
yield
np.set_printoptions(**oldoptions)
class RGBDFrame():
def load(self, file_handle):
self.camera_to_world = np.asarray(struct.unpack('f'*16, file_handle.read(16*4)), dtype=np.float32).reshape(4, 4)
self.timestamp_color = struct.unpack('Q', file_handle.read(8))[0]
self.timestamp_depth = struct.unpack('Q', file_handle.read(8))[0]
self.color_size_bytes = struct.unpack('Q', file_handle.read(8))[0]
self.depth_size_bytes = struct.unpack('Q', file_handle.read(8))[0]
self.color_data = b''.join(struct.unpack('c'*self.color_size_bytes, file_handle.read(self.color_size_bytes)))
self.depth_data = b''.join(struct.unpack('c'*self.depth_size_bytes, file_handle.read(self.depth_size_bytes)))
def decompress_depth(self, compression_type):
if compression_type == 'zlib_ushort':
return self.decompress_depth_zlib()
else:
raise
def decompress_depth_zlib(self):
return zlib.decompress(self.depth_data)
def decompress_color(self, compression_type):
if compression_type == 'jpeg':
return self.decompress_color_jpeg()
else:
raise
def dump_color_to_file(self, compression_type, filepath):
if compression_type == 'jpeg':
filepath += ".jpg"
else:
raise
f = open(filepath, "wb")
f.write(self.color_data)
f.close()
def decompress_color_jpeg(self):
return imageio.imread(self.color_data)
class SensorData:
def __init__(self, filename):
self.version = 4
self.load(filename)
def load(self, filename):
with open(filename, 'rb') as f:
version = struct.unpack('I', f.read(4))[0]
assert self.version == version
strlen = struct.unpack('Q', f.read(8))[0]
self.sensor_name = b''.join(struct.unpack('c'*strlen, f.read(strlen)))
self.intrinsic_color = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4)
self.extrinsic_color = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4)
self.intrinsic_depth = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4)
self.extrinsic_depth = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4)
self.color_compression_type = COMPRESSION_TYPE_COLOR[struct.unpack('i', f.read(4))[0]]
self.depth_compression_type = COMPRESSION_TYPE_DEPTH[struct.unpack('i', f.read(4))[0]]
self.color_width = struct.unpack('I', f.read(4))[0]
self.color_height = struct.unpack('I', f.read(4))[0]
self.depth_width = struct.unpack('I', f.read(4))[0]
self.depth_height = struct.unpack('I', f.read(4))[0]
self.depth_shift = struct.unpack('f', f.read(4))[0]
self.num_frames = struct.unpack('Q', f.read(8))[0]
self.frames = []
for i in range(self.num_frames):
frame = RGBDFrame()
frame.load(f)
self.frames.append(frame)
self.num_IMU_frames = struct.unpack('Q', f.read(8))[0]
def export_depth_images(self, output_path, image_size=None, frame_skip=1):
if not os.path.exists(output_path):
os.makedirs(output_path)
print('exporting', len(self.frames), frame_skip, ' depth frames to', output_path)
for f in range(0, len(self.frames), frame_skip):
depth_data = self.frames[f].decompress_depth(self.depth_compression_type)
depth = np.fromstring(depth_data, dtype=np.uint16).reshape(self.depth_height, self.depth_width)
if image_size is not None:
depth = cv2.resize(depth, (image_size[0], image_size[1]), interpolation=cv2.INTER_NEAREST)
filepath = os.path.join(output_path, f"frame-{f:06d}.depth.{int(image_size[0])}.png")
else:
filepath = os.path.join(output_path, f"frame-{f:06d}.depth.png")
with open(filepath, 'wb') as f: # write 16-bit
writer = png.Writer(width=depth.shape[1], height=depth.shape[0], bitdepth=16)
depth = depth.reshape(-1, depth.shape[1]).tolist()
writer.write(f, depth)
def export_color_images(self, output_path, image_size=None, frame_skip=1):
if not os.path.exists(output_path):
os.makedirs(output_path)
print('exporting', len(self.frames), frame_skip, 'color frames to', output_path)
for f in range(0, len(self.frames), frame_skip):
color = self.frames[f].decompress_color(self.color_compression_type)
if image_size is not None:
resized = Image.fromarray(color).resize((image_size[0], image_size[1]), resample=Image.BILINEAR)
filepath = os.path.join(output_path, f"frame-{f:06d}.color.{int(image_size[0])}.png")
resized.save(filepath)
else:
filepath = os.path.join(output_path, f"frame-{f:06d}.color")
self.frames[f].dump_color_to_file(self.color_compression_type, filepath)
def save_mat_to_file(self, matrix, filename):
with open(filename, 'w') as f:
for line in matrix:
np.savetxt(f, line[np.newaxis], fmt='%f')
def export_poses(self, output_path, frame_skip=1):
if not os.path.exists(output_path):
os.makedirs(output_path)
print('exporting', len(self.frames), frame_skip, 'camera poses to', output_path)
for f in range(0, len(self.frames), frame_skip):
self.save_mat_to_file(self.frames[f].camera_to_world, os.path.join(output_path, f"frame-{f:06d}.pose.txt"))
def export_intrinsics(self, output_path, scan_name):
default_intrinsics_path = os.path.join(output_path, 'intrinsic')
if not os.path.exists(default_intrinsics_path):
os.makedirs(default_intrinsics_path)
print('exporting camera intrinsics to', default_intrinsics_path)
self.save_mat_to_file(self.intrinsic_color, os.path.join(default_intrinsics_path, 'intrinsic_color.txt'))
self.save_mat_to_file(self.extrinsic_color, os.path.join(default_intrinsics_path, 'extrinsic_color.txt'))
self.save_mat_to_file(self.intrinsic_depth, os.path.join(default_intrinsics_path, 'intrinsic_depth.txt'))
self.save_mat_to_file(self.extrinsic_depth, os.path.join(default_intrinsics_path, 'extrinsic_depth.txt'))
================================================
FILE: data_scripts/scannet_wrangling_scripts/download_scannet.py
================================================
#!/usr/bin/env python
# Downloads ScanNet public data release
# Run with ./download-scannet.py (or python download-scannet.py on Windows)
# -*- coding: utf-8 -*-
import argparse
import os
import urllib.request
import tempfile
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
BASE_URL = 'http://kaldir.vc.in.tum.de/scannet/'
TOS_URL = BASE_URL + 'ScanNet_TOS.pdf'
FILETYPES = ['.aggregation.json', '.sens', '.txt', '_vh_clean.ply', '_vh_clean_2.0.010000.segs.json', '_vh_clean_2.ply', '_vh_clean.segs.json', '_vh_clean.aggregation.json', '_vh_clean_2.labels.ply', '_2d-instance.zip', '_2d-instance-filt.zip', '_2d-label.zip', '_2d-label-filt.zip']
FILETYPES_TEST = ['.sens', '.txt', '_vh_clean.ply', '_vh_clean_2.ply']
PREPROCESSED_FRAMES_FILE = ['scannet_frames_25k.zip', '5.6GB']
TEST_FRAMES_FILE = ['scannet_frames_test.zip', '610MB']
LABEL_MAP_FILES = ['scannetv2-labels.combined.tsv', 'scannet-labels.combined.tsv']
DATA_EFFICIENT_FILES = ['limited-reconstruction-scenes.zip', 'limited-annotation-points.zip', 'limited-bboxes.zip', '1.7MB']
GRIT_FILES = ['ScanNet-GRIT.zip']
RELEASES = ['v2/scans', 'v1/scans']
RELEASES_TASKS = ['v2/tasks', 'v1/tasks']
RELEASES_NAMES = ['v2', 'v1']
RELEASE = RELEASES[0]
RELEASE_TASKS = RELEASES_TASKS[0]
RELEASE_NAME = RELEASES_NAMES[0]
LABEL_MAP_FILE = LABEL_MAP_FILES[0]
RELEASE_SIZE = '1.2TB'
V1_IDX = 1
def get_release_scans(release_file):
scan_lines = urllib.request.urlopen(release_file)
scans = []
for scan_line in scan_lines:
scan_id = scan_line.decode('utf8').rstrip('\n')
scans.append(scan_id)
return scans
def download_release(release_scans, out_dir, file_types, use_v1_sens):
if len(release_scans) == 0:
return
print('Downloading ScanNet ' + RELEASE_NAME + ' release to ' + out_dir + '...')
for scan_id in release_scans:
scan_out_dir = os.path.join(out_dir, scan_id)
download_scan(scan_id, scan_out_dir, file_types, use_v1_sens)
print('Downloaded ScanNet ' + RELEASE_NAME + ' release.')
def download_file(url, out_file):
out_dir = os.path.dirname(out_file)
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
if not os.path.isfile(out_file):
print('\t' + url + ' > ' + out_file)
fh, out_file_tmp = tempfile.mkstemp(dir=out_dir)
f = os.fdopen(fh, 'w')
f.close()
urllib.request.urlretrieve(url, out_file_tmp)
os.rename(out_file_tmp, out_file)
else:
print('WARNING: skipping download of existing file ' + out_file)
def download_scan(scan_id, out_dir, file_types, use_v1_sens):
print('Downloading ScanNet ' + RELEASE_NAME + ' scan ' + scan_id + ' ...')
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
for ft in file_types:
v1_sens = use_v1_sens and ft == '.sens'
url = BASE_URL + RELEASE + '/' + scan_id + '/' + scan_id + ft if not v1_sens else BASE_URL + RELEASES[V1_IDX] + '/' + scan_id + '/' + scan_id + ft
out_file = out_dir + '/' + scan_id + ft
download_file(url, out_file)
print('Downloaded scan ' + scan_id)
def download_task_data(out_dir):
print('Downloading ScanNet v1 task data...')
files = [
LABEL_MAP_FILES[V1_IDX], 'obj_classification/data.zip',
'obj_classification/trained_models.zip', 'voxel_labeling/data.zip',
'voxel_labeling/trained_models.zip'
]
for file in files:
url = BASE_URL + RELEASES_TASKS[V1_IDX] + '/' + file
localpath = os.path.join(out_dir, file)
localdir = os.path.dirname(localpath)
if not os.path.isdir(localdir):
os.makedirs(localdir)
download_file(url, localpath)
print('Downloaded task data.')
def download_tfrecords(in_dir, out_dir):
print('Downloading tf records (302 GB)...')
if not os.path.exists(out_dir):
os.makedirs(out_dir)
split_to_num_shards = {'train': 100, 'val': 25, 'test': 10}
for folder_name in ['hires_tfrecords', 'lores_tfrecords']:
folder_dir = '%s/%s' % (in_dir, folder_name)
save_dir = '%s/%s' % (out_dir, folder_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for split, num_shards in split_to_num_shards.items():
for i in range(num_shards):
file_name = '%s-%05d-of-%05d.tfrecords' % (split, i, num_shards)
url = '%s/%s' % (folder_dir, file_name)
localpath = '%s/%s/%s' % (out_dir, folder_name, file_name)
download_file(url, localpath)
def download_label_map(out_dir):
print('Downloading ScanNet ' + RELEASE_NAME + ' label mapping file...')
files = [ LABEL_MAP_FILE ]
for file in files:
url = BASE_URL + RELEASE_TASKS + '/' + file
localpath = os.path.join(out_dir, file)
localdir = os.path.dirname(localpath)
if not os.path.isdir(localdir):
os.makedirs(localdir)
download_file(url, localpath)
print('Downloaded ScanNet ' + RELEASE_NAME + ' label mapping file.')
def main():
parser = argparse.ArgumentParser(description='Downloads ScanNet public data release.')
parser.add_argument('-o', '--out_dir', required=True, help='directory in which to download')
parser.add_argument('--task_data', action='store_true', help='download task data (v1)')
parser.add_argument('--label_map', action='store_true', help='download label map file')
parser.add_argument('--v1', action='store_true', help='download ScanNet v1 instead of v2')
parser.add_argument('--id', help='specific scan id to download')
parser.add_argument('--preprocessed_frames', action='store_true', help='download preprocessed subset of ScanNet frames (' + PREPROCESSED_FRAMES_FILE[1] + ')')
parser.add_argument('--test_frames_2d', action='store_true', help='download 2D test frames (' + TEST_FRAMES_FILE[1] + '; also included with whole dataset download)')
parser.add_argument('--data_efficient', action='store_true', help='download data efficient task files; also included with whole dataset download)')
parser.add_argument('--tf_semantic', action='store_true', help='download google tensorflow records for 3D segmentation / detection')
parser.add_argument('--grit', action='store_true', help='download ScanNet files for General Robust Image Task')
parser.add_argument('--type', help='specific file type to download (.aggregation.json, .sens, .txt, _vh_clean.ply, _vh_clean_2.0.010000.segs.json, _vh_clean_2.ply, _vh_clean.segs.json, _vh_clean.aggregation.json, _vh_clean_2.labels.ply, _2d-instance.zip, _2d-instance-filt.zip, _2d-label.zip, _2d-label-filt.zip)')
args = parser.parse_args()
print('By pressing any key to continue you confirm that you have agreed to the ScanNet terms of use as described at:')
print(TOS_URL)
print('***')
print('Press any key to continue, or CTRL-C to exit.')
key = input('')
if args.v1:
global RELEASE
global RELEASE_TASKS
global RELEASE_NAME
global LABEL_MAP_FILE
RELEASE = RELEASES[V1_IDX]
RELEASE_TASKS = RELEASES_TASKS[V1_IDX]
RELEASE_NAME = RELEASES_NAMES[V1_IDX]
LABEL_MAP_FILE = LABEL_MAP_FILES[V1_IDX]
assert((not args.tf_semantic) and (not args.grit)), "Task files specified invalid for v1"
release_file = BASE_URL + RELEASE + '.txt'
release_scans = get_release_scans(release_file)
file_types = FILETYPES;
release_test_file = BASE_URL + RELEASE + '_test.txt'
release_test_scans = get_release_scans(release_test_file)
file_types_test = FILETYPES_TEST;
out_dir_scans = os.path.join(args.out_dir, 'scans')
out_dir_test_scans = os.path.join(args.out_dir, 'scans_test')
out_dir_tasks = os.path.join(args.out_dir, 'tasks')
if args.type: # download file type
file_type = args.type
if file_type not in FILETYPES:
print('ERROR: Invalid file type: ' + file_type)
return
file_types = [file_type]
if file_type in FILETYPES_TEST:
file_types_test = [file_type]
else:
file_types_test = []
if args.task_data: # download task data
download_task_data(out_dir_tasks)
elif args.label_map: # download label map file
download_label_map(args.out_dir)
elif args.preprocessed_frames: # download preprocessed scannet_frames_25k.zip file
if args.v1:
print('ERROR: Preprocessed frames only available for ScanNet v2')
print('You are downloading the preprocessed subset of frames ' + PREPROCESSED_FRAMES_FILE[0] + ' which requires ' + PREPROCESSED_FRAMES_FILE[1] + ' of space.')
download_file(os.path.join(BASE_URL, RELEASE_TASKS, PREPROCESSED_FRAMES_FILE[0]), os.path.join(out_dir_tasks, PREPROCESSED_FRAMES_FILE[0]))
elif args.test_frames_2d: # download test scannet_frames_test.zip file
if args.v1:
print('ERROR: 2D test frames only available for ScanNet v2')
print('You are downloading the 2D test set ' + TEST_FRAMES_FILE[0] + ' which requires ' + TEST_FRAMES_FILE[1] + ' of space.')
download_file(os.path.join(BASE_URL, RELEASE_TASKS, TEST_FRAMES_FILE[0]), os.path.join(out_dir_tasks, TEST_FRAMES_FILE[0]))
elif args.data_efficient: # download data efficient task files
print('You are downloading the data efficient task files' + ' which requires ' + DATA_EFFICIENT_FILES[-1] + ' of space.')
for k in range(len(DATA_EFFICIENT_FILES)-1):
download_file(os.path.join(BASE_URL, RELEASE_TASKS, DATA_EFFICIENT_FILES[k]), os.path.join(out_dir_tasks, DATA_EFFICIENT_FILES[k]))
elif args.tf_semantic: # download google tf records
download_tfrecords(os.path.join(BASE_URL, RELEASE_TASKS, 'tf3d'), os.path.join(out_dir_tasks, 'tf3d'))
elif args.grit: # download GRIT file
download_file(os.path.join(BASE_URL, RELEASE_TASKS, GRIT_FILES[0]), os.path.join(out_dir_tasks, GRIT_FILES[0]))
elif args.id: # download single scan
scan_id = args.id
is_test_scan = scan_id in release_test_scans
if scan_id not in release_scans and (not is_test_scan or args.v1):
print('ERROR: Invalid scan id: ' + scan_id)
else:
out_dir = os.path.join(out_dir_scans, scan_id) if not is_test_scan else os.path.join(out_dir_test_scans, scan_id)
scan_file_types = file_types if not is_test_scan else file_types_test
use_v1_sens = not is_test_scan
if not is_test_scan and not args.v1 and '.sens' in scan_file_types:
print('Note: ScanNet v2 uses the same .sens files as ScanNet v1: Press \'n\' to exclude downloading .sens files for each scan')
key = input('')
if key.strip().lower() == 'n':
scan_file_types.remove('.sens')
download_scan(scan_id, out_dir, scan_file_types, use_v1_sens)
else: # download entire release
if len(file_types) == len(FILETYPES):
print('WARNING: You are downloading the entire ScanNet ' + RELEASE_NAME + ' release which requires ' + RELEASE_SIZE + ' of space.')
else:
print('WARNING: You are downloading all ScanNet ' + RELEASE_NAME + ' scans of type ' + file_types[0])
print('Note that existing scan directories will be skipped. Delete partially downloaded directories to re-download.')
print('***')
print('Press any key to continue, or CTRL-C to exit.')
key = input('')
if not args.v1 and '.sens' in file_types:
print('Note: ScanNet v2 uses the same .sens files as ScanNet v1: Press \'n\' to exclude downloading .sens files for each scan')
key = input('')
if key.strip().lower() == 'n':
file_types.remove('.sens')
download_release(release_scans, out_dir_scans, file_types, use_v1_sens=True)
if not args.v1:
download_label_map(args.out_dir)
download_release(release_test_scans, out_dir_test_scans, file_types_test, use_v1_sens=False)
download_file(os.path.join(BASE_URL, RELEASE_TASKS, TEST_FRAMES_FILE[0]), os.path.join(out_dir_tasks, TEST_FRAMES_FILE[0]))
for k in range(len(DATA_EFFICIENT_FILES)-1):
download_file(os.path.join(BASE_URL, RELEASE_TASKS, DATA_EFFICIENT_FILES[k]), os.path.join(out_dir_tasks, DATA_EFFICIENT_FILES[k]))
if __name__ == "__main__": main()
================================================
FILE: data_scripts/scannet_wrangling_scripts/env.yml
================================================
name: scannet_extraction
dependencies:
- python=3.9.7
- numpy
- imageio
- pillow
- tqdm
- pip
- pip:
- opencv-python
- pypng
================================================
FILE: data_scripts/scannet_wrangling_scripts/reader.py
================================================
import argparse
from concurrent.futures import process
import os, sys
from tqdm import tqdm
from multiprocessing.pool import Pool
from functools import partial
from multiprocessing import Manager
from SensorData import SensorData
# params
parser = argparse.ArgumentParser()
# data paths
parser.add_argument('--scans_folder', required=True, help='dataset root')
parser.add_argument('--scan_list_file', required=False, default=None, help='scan list file')
parser.add_argument('--single_debug_scan_id', required=False, default=None, help='single scan to debug')
parser.add_argument('--output_path', required=True, help='path to output folder')
parser.add_argument('--export_depth_images', dest='export_depth_images', action='store_true')
parser.add_argument('--export_color_images', dest='export_color_images', action='store_true')
parser.add_argument('--export_poses', dest='export_poses', action='store_true')
parser.add_argument('--export_intrinsics', dest='export_intrinsics', action='store_true')
parser.add_argument('--num_workers', type=int, default=1)
parser.add_argument('--rgb_resize', nargs='+', type=int, default=None, help='width height')
parser.add_argument('--depth_resize', nargs='+', type=int, default=None, help='width height')
parser.set_defaults(export_depth_images=False, export_color_images=False, export_poses=False, export_intrinsics=False)
opt = parser.parse_args()
print(opt)
def process_scan(opt, scan_job, count=None, progress=None):
filename = scan_job[0]
output_path = scan_job[1]
scan_name = scan_job[2]
if not os.path.exists(output_path):
os.makedirs(output_path)
# load the data
sys.stdout.write('loading %s...' % opt.scans_folder)
sd = SensorData(filename)
sys.stdout.write('loaded!\n')
if opt.export_depth_images:
sd.export_depth_images(os.path.join(output_path, 'sensor_data'), image_size=opt.depth_resize)
if opt.export_color_images:
sd.export_color_images(os.path.join(output_path, 'sensor_data'), image_size=opt.rgb_resize)
if opt.export_poses:
sd.export_poses(os.path.join(output_path, 'sensor_data'))
if opt.export_intrinsics:
sd.export_intrinsics(output_path, scan_name)
if progress is not None:
progress.value += 1
print(f"Completed scan {filename}, {progress.value} of total {count}.")
def main():
if opt.single_debug_scan_id is not None:
scans = [opt.single_debug_scan_id]
else:
f = open(opt.scan_list_file, "r")
scans = f.readlines()
scans = [scan.strip() for scan in scans]
input_files = [os.path.join(opt.scans_folder, f"{scan}/{scan}.sens") for
scan in scans]
output_dirs = [os.path.join(opt.output_path, scan) for
scan in scans]
scan_jobs = list(zip(input_files, output_dirs, scans))
if opt.num_workers == 1:
for scan_job in tqdm(scan_jobs):
process_scan(opt, scan_job)
else:
pool = Pool(opt.num_workers)
manager = Manager()
count = len(scan_jobs)
progress = manager.Value('i', 0)
pool.map(
partial(
process_scan,
opt,
count=count,
progress=progress
),
scan_jobs,
)
if __name__ == '__main__':
main()
================================================
FILE: data_scripts/scannet_wrangling_scripts/splits/scannetv2_test.txt
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FILE: data_scripts/scannet_wrangling_scripts/splits/scannetv2_train.txt
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FILE: data_scripts/scannet_wrangling_scripts/splits/scannetv2_val.txt
================================================
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FILE: data_splits/7Scenes/dvmvs_split/dvmvs_test_split.txt
================================================
redkitchen/seq-01
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================================================
FILE: data_splits/7Scenes/dvmvs_split/test_eight_view_deepvmvs.txt
================================================
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gitextract_6sfl0utb/
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── configs/
│ ├── data/
│ │ ├── 7scenes_default.yaml
│ │ ├── 7scenes_dense.yaml
│ │ ├── colmap_dense.yaml
│ │ ├── neucon_arkit_default.yaml
│ │ ├── neucon_arkit_dense.yaml
│ │ ├── scannet_default_test.yaml
│ │ ├── scannet_default_train.yaml
│ │ ├── scannet_default_val.yaml
│ │ ├── scannet_dense_test.yaml
│ │ ├── scannet_dense_val.yaml
│ │ ├── scanniverse_dense.yaml
│ │ ├── vdr_default.yaml
│ │ ├── vdr_dense.yaml
│ │ ├── vdr_dense_offline.yaml
│ │ └── vdr_dense_rerun.yaml
│ └── models/
│ ├── dot_product_model.yaml
│ └── hero_model.yaml
├── data_scripts/
│ ├── 7scenes_preprocessing.py
│ ├── IOS_LOGGER_ARKIT_README.md
│ ├── generate_test_tuples.py
│ ├── generate_train_tuples.py
│ ├── ios_logger_preprocessing.py
│ ├── precompute_valid_frames.py
│ └── scannet_wrangling_scripts/
│ ├── LICENSE
│ ├── README.md
│ ├── SensorData.py
│ ├── download_scannet.py
│ ├── env.yml
│ ├── reader.py
│ └── splits/
│ ├── scannetv2_test.txt
│ ├── scannetv2_train.txt
│ └── scannetv2_val.txt
├── data_splits/
│ ├── 7Scenes/
│ │ └── dvmvs_split/
│ │ ├── dvmvs_test_split.txt
│ │ └── test_eight_view_deepvmvs.txt
│ ├── ScanNetv2/
│ │ ├── dvmvs_split/
│ │ │ ├── dvmvs_train.txt
│ │ │ ├── dvmvs_val.txt
│ │ │ ├── test_eight_view_deepvmvs.txt
│ │ │ └── test_eight_view_deepvmvs_dense.txt
│ │ └── standard_split/
│ │ ├── scannetv2_test.txt
│ │ ├── scannetv2_train.txt
│ │ ├── scannetv2_val.txt
│ │ ├── test_eight_view_deepvmvs.txt
│ │ ├── test_eight_view_deepvmvs_dense.txt
│ │ ├── test_eight_view_deepvmvs_offline.txt
│ │ ├── train_eight_view_deepvmvs.txt
│ │ └── val_eight_view_deepvmvs.txt
│ ├── arkit/
│ │ ├── scans.txt
│ │ ├── test_eight_view_deepvmvs.txt
│ │ └── test_eight_view_deepvmvs_dense.txt
│ └── vdr/
│ ├── scans.txt
│ ├── test_eight_view_deepvmvs.txt
│ ├── test_eight_view_deepvmvs_dense.txt
│ └── test_eight_view_deepvmvs_dense_offline.txt
├── experiment_modules/
│ └── depth_model.py
├── losses.py
├── modules/
│ ├── cost_volume.py
│ ├── layers.py
│ └── networks.py
├── options.py
├── pc_fusion.py
├── pixi.toml
├── requirements.txt
├── rerun_visualize_live_meshing.py
├── simplerecon_env.yml
├── sr_datasets/
│ ├── arkit_dataset.py
│ ├── colmap_dataset.py
│ ├── generic_mvs_dataset.py
│ ├── scannet_dataset.py
│ ├── scanniverse_dataset.py
│ ├── seven_scenes_dataset.py
│ └── vdr_dataset.py
├── test.py
├── tools/
│ ├── fusers_helper.py
│ ├── keyframe_buffer.py
│ ├── mesh_renderer.py
│ ├── torch_point_cloud_fusion.py
│ └── tsdf.py
├── train.py
├── utils/
│ ├── dataset_utils.py
│ ├── generic_utils.py
│ ├── geometry_utils.py
│ ├── metrics_utils.py
│ └── visualization_utils.py
├── visualization_scripts/
│ ├── generate_gt_min_max_cache.py
│ ├── load_meshes_and_include_normals.py
│ └── visualize_scene_depth_output.py
├── visualize_live_meshing.py
└── weights/
└── strip_checkpoint.py
SYMBOL INDEX (370 symbols across 37 files)
FILE: data_scripts/7scenes_preprocessing.py
function mkdir (line 34) | def mkdir(directory):
function process_scene (line 62) | def process_scene(ds):
FILE: data_scripts/generate_test_tuples.py
function compute_offline_tuple (line 65) | def compute_offline_tuple(
function default_dvmvs_tuples (line 159) | def default_dvmvs_tuples(scan, poses, dists_to_last_valid, n_measurement...
function offline_dvmvs_tuples (line 214) | def offline_dvmvs_tuples(scan, poses, n_measurement_frames):
function dense_dvmvs_tuples (line 268) | def dense_dvmvs_tuples(scan, poses, n_measurement_frames):
function offline_dense_dvmvs_tuples (line 345) | def offline_dense_dvmvs_tuples(scan, poses, n_measurement_frames):
function crawl_subprocess_long (line 384) | def crawl_subprocess_long(opts_temp_filepath, scan, count, progress):
function crawl (line 530) | def crawl(opts_temp_filepath, opts, scans):
FILE: data_scripts/generate_train_tuples.py
function gather_pairs_train (line 55) | def gather_pairs_train(poses, used_pairs,
function crawl_subprocess_short (line 140) | def crawl_subprocess_short(opts_temp_filepath, scan, count, progress):
function crawl_subprocess_long (line 221) | def crawl_subprocess_long(opts_temp_filepath, scan, count, progress):
function crawl (line 378) | def crawl(opts_temp_filepath, opts, scans):
FILE: data_scripts/precompute_valid_frames.py
function process_scan (line 45) | def process_scan(opts_temp_filepath, scan, count, progress):
function multi_process_scans (line 92) | def multi_process_scans(opts_temp_filepath, opts, scans):
FILE: data_scripts/scannet_wrangling_scripts/SensorData.py
function print_array_on_one_line (line 16) | def print_array_on_one_line():
class RGBDFrame (line 23) | class RGBDFrame():
method load (line 25) | def load(self, file_handle):
method decompress_depth (line 35) | def decompress_depth(self, compression_type):
method decompress_depth_zlib (line 42) | def decompress_depth_zlib(self):
method decompress_color (line 46) | def decompress_color(self, compression_type):
method dump_color_to_file (line 52) | def dump_color_to_file(self, compression_type, filepath):
method decompress_color_jpeg (line 61) | def decompress_color_jpeg(self):
class SensorData (line 65) | class SensorData:
method __init__ (line 67) | def __init__(self, filename):
method load (line 72) | def load(self, filename):
method export_depth_images (line 98) | def export_depth_images(self, output_path, image_size=None, frame_skip...
method export_color_images (line 117) | def export_color_images(self, output_path, image_size=None, frame_skip...
method save_mat_to_file (line 133) | def save_mat_to_file(self, matrix, filename):
method export_poses (line 139) | def export_poses(self, output_path, frame_skip=1):
method export_intrinsics (line 147) | def export_intrinsics(self, output_path, scan_name):
FILE: data_scripts/scannet_wrangling_scripts/download_scannet.py
function get_release_scans (line 33) | def get_release_scans(release_file):
function download_release (line 42) | def download_release(release_scans, out_dir, file_types, use_v1_sens):
function download_file (line 52) | def download_file(url, out_file):
function download_scan (line 66) | def download_scan(scan_id, out_dir, file_types, use_v1_sens):
function download_task_data (line 78) | def download_task_data(out_dir):
function download_tfrecords (line 94) | def download_tfrecords(in_dir, out_dir):
function download_label_map (line 112) | def download_label_map(out_dir):
function main (line 125) | def main():
FILE: data_scripts/scannet_wrangling_scripts/reader.py
function process_scan (line 30) | def process_scan(opt, scan_job, count=None, progress=None):
function main (line 54) | def main():
FILE: experiment_modules/depth_model.py
class DepthModel (line 21) | class DepthModel(pl.LightningModule):
method __init__ (line 68) | def __init__(self, opts):
method compute_matching_feats (line 191) | def compute_matching_feats(
method forward (line 247) | def forward(
method compute_losses (line 409) | def compute_losses(self, cur_data, src_data, outputs):
method step (line 502) | def step(self, phase, batch, batch_idx):
method training_step (line 607) | def training_step(self, batch, batch_idx):
method validation_step (line 611) | def validation_step(self, batch, batch_idx):
method configure_optimizers (line 615) | def configure_optimizers(self):
FILE: losses.py
class MSGradientLoss (line 11) | class MSGradientLoss(nn.Module):
method __init__ (line 12) | def __init__(self, num_scales: int = 4):
method forward (line 17) | def forward(self, depth_gt: Tensor, depth_pred: Tensor) -> Tensor:
class ScaleInvariantLoss (line 39) | class ScaleInvariantLoss(jit.ScriptModule):
method __init__ (line 40) | def __init__(self, si_lambda: float = 0.85):
method forward (line 46) | def forward(self, log_depth_gt: Tensor, log_depth_pred: Tensor) -> Ten...
class NormalsLoss (line 57) | class NormalsLoss(nn.Module):
method forward (line 58) | def forward(self, normals_gt_b3hw: Tensor, normals_pred_b3hw: Tensor) ...
class MVDepthLoss (line 79) | class MVDepthLoss(nn.Module):
method __init__ (line 80) | def __init__(self, height, width):
method get_valid_mask (line 90) | def get_valid_mask(
method get_error_for_pair (line 136) | def get_error_for_pair(self,
method forward (line 175) | def forward(
FILE: modules/cost_volume.py
class CostVolumeManager (line 13) | class CostVolumeManager(nn.Module):
method __init__ (line 27) | def __init__(
method initialise_for_projection (line 58) | def initialise_for_projection(self):
method get_mask (line 77) | def get_mask(self, pix_coords_bk2hw):
method generate_depth_planes (line 100) | def generate_depth_planes(self, batch_size: int,
method warp_features (line 139) | def warp_features(
method build_cost_volume (line 237) | def build_cost_volume(
method indices_to_disparity (line 338) | def indices_to_disparity(self, indices, depth_planes_bdhw):
method forward (line 345) | def forward(
class FeatureVolumeManager (line 383) | class FeatureVolumeManager(CostVolumeManager):
method __init__ (line 398) | def __init__(self,
method build_cost_volume (line 451) | def build_cost_volume(self,
method to_fast (line 739) | def to_fast(self) -> 'FastFeatureVolumeManager':
class FastFeatureVolumeManager (line 749) | class FastFeatureVolumeManager(FeatureVolumeManager):
method __init__ (line 759) | def __init__(self,
method warp_features (line 812) | def warp_features(
method build_cost_volume (line 967) | def build_cost_volume(
FILE: modules/layers.py
function conv3x3 (line 7) | def conv3x3(
function conv1x1 (line 20) | def conv1x1(in_planes: int, out_planes: int, stride: int = 1, bias: bool...
class BasicBlock (line 24) | class BasicBlock(nn.Module):
method __init__ (line 27) | def __init__(
method forward (line 68) | def forward(self, x: Tensor) -> Tensor:
class TensorFormatter (line 87) | class TensorFormatter(nn.Module):
method __init__ (line 95) | def __init__(self):
method _expand_batch_with_channels (line 101) | def _expand_batch_with_channels(self, x):
method _reduce_batch_to_channels (line 109) | def _reduce_batch_to_channels(self, x):
method forward (line 117) | def forward(self, x, apply_func):
FILE: modules/networks.py
function double_basic_block (line 13) | def double_basic_block(num_ch_in, num_ch_out, num_repeats=2):
class DepthDecoderPP (line 20) | class DepthDecoderPP(nn.Module):
method __init__ (line 21) | def __init__(
method forward (line 75) | def forward(self, input_features):
class CVEncoder (line 99) | class CVEncoder(nn.Module):
method __init__ (line 100) | def __init__(self, num_ch_cv, num_ch_enc, num_ch_outs):
method forward (line 120) | def forward(self, x, img_feats):
class MLP (line 129) | class MLP(nn.Module):
method __init__ (line 130) | def __init__(self, channel_list, disable_final_activation = False):
method forward (line 146) | def forward(self, x):
class ResnetMatchingEncoder (line 149) | class ResnetMatchingEncoder(nn.Module):
method __init__ (line 152) | def __init__(
method forward (line 204) | def forward(self, input_image):
class UNetMatchingEncoder (line 207) | class UNetMatchingEncoder(nn.Module):
method __init__ (line 208) | def __init__(self):
method forward (line 226) | def forward(self, x):
FILE: options.py
class Options (line 10) | class Options():
class OptionsHandler (line 220) | class OptionsHandler():
method __init__ (line 256) | def __init__(self, required_flags=[]):
method parse_and_merge_options (line 271) | def parse_and_merge_options(self, config_filepaths=None, ignore_cl_arg...
method populate_argparse (line 336) | def populate_argparse(self):
method check_required_items (line 350) | def check_required_items(self):
method merge_config_options (line 356) | def merge_config_options(self, config_options):
method merge_cl_args (line 364) | def merge_cl_args(self, cl_args):
method pretty_print_options (line 384) | def pretty_print_options(self):
method load_options_from_yaml (line 393) | def load_options_from_yaml(config_filepath):
method save_options_as_yaml (line 398) | def save_options_as_yaml(config_filepath, options):
function handle_backwards_compat (line 402) | def handle_backwards_compat(opts):
FILE: pc_fusion.py
function main (line 34) | def main(opts):
FILE: rerun_visualize_live_meshing.py
function to_device (line 32) | def to_device(input_dict, key_ignores=[], device="cuda"):
function log_source_data (line 42) | def log_source_data(src_entity_path: str, src_data: Dict[str, Any]) -> N...
function log_camera (line 55) | def log_camera(
function log_image (line 76) | def log_image(
function log_rerun (line 92) | def log_rerun(
function main (line 168) | def main(opts):
FILE: sr_datasets/arkit_dataset.py
class ARKitDataset (line 16) | class ARKitDataset(GenericMVSDataset):
method __init__ (line 28) | def __init__(self,
method get_sub_folder_dir (line 125) | def get_sub_folder_dir(split):
method get_frame_id_string (line 128) | def get_frame_id_string(self, frame_id):
method get_valid_frame_path (line 137) | def get_valid_frame_path(self, split, scan):
method get_valid_frame_ids (line 145) | def get_valid_frame_ids(self, split, scan, store_computed=True):
method get_color_filepath (line 240) | def get_color_filepath(self, scan_id, frame_id):
method get_high_res_color_filepath (line 265) | def get_high_res_color_filepath(self, scan_id, frame_id):
method get_cached_depth_filepath (line 290) | def get_cached_depth_filepath(self, scan_id, frame_id):
method get_full_res_depth_filepath (line 299) | def get_full_res_depth_filepath(self, scan_id, frame_id):
method get_pose_filepath (line 307) | def get_pose_filepath(self, scan_id, frame_id):
method load_target_size_depth_and_mask (line 321) | def load_target_size_depth_and_mask(self, scan_id, frame_id):
method load_full_res_depth_and_mask (line 335) | def load_full_res_depth_and_mask(self, scan_id, frame_id):
method load_pose (line 350) | def load_pose(self, scan_id, frame_id):
method load_intrinsics (line 372) | def load_intrinsics(self, scan_id, frame_id, flip=None):
function process_data (line 421) | def process_data(data_path, data_source='ARKit',
function path_parser (line 467) | def path_parser(root_path, data_source='TagBA'):
function load_camera_pose (line 484) | def load_camera_pose(cam_pose_dir, use_homogenous=True, data_source='Tag...
function load_camera_intrinsic (line 536) | def load_camera_intrinsic(cam_file, data_source='TagBA'):
function extract_frames (line 597) | def extract_frames(video_path, out_folder, size):
function sync_intrinsics_and_poses (line 608) | def sync_intrinsics_and_poses(cam_file, pose_file, out_file):
FILE: sr_datasets/colmap_dataset.py
class ColmapDataset (line 15) | class ColmapDataset(GenericMVSDataset):
method __init__ (line 48) | def __init__(
method get_sub_folder_dir (line 142) | def get_sub_folder_dir(split):
method get_frame_id_string (line 145) | def get_frame_id_string(self, frame_id):
method get_valid_frame_path (line 154) | def get_valid_frame_path(self, split, scan):
method get_valid_frame_ids (line 163) | def get_valid_frame_ids(self, split, scan, store_computed=True):
method load_pose (line 233) | def load_pose(self, scan_id, frame_id):
method load_intrinsics (line 268) | def load_intrinsics(self, scan_id, frame_id=None, flip=None):
method load_capture_poses (line 398) | def load_capture_poses(self, scan_id):
method load_target_size_depth_and_mask (line 448) | def load_target_size_depth_and_mask(self, scan_id, frame_id):
method load_full_res_depth_and_mask (line 462) | def load_full_res_depth_and_mask(self, scan_id, frame_id):
method get_cached_depth_filepath (line 477) | def get_cached_depth_filepath(self, scan_id, frame_id):
method get_full_res_depth_filepath (line 487) | def get_full_res_depth_filepath(self, scan_id, frame_id):
method get_color_filepath (line 495) | def get_color_filepath(self, scan_id, frame_id):
method get_high_res_color_filepath (line 522) | def get_high_res_color_filepath(self, scan_id, frame_id):
method load_color (line 543) | def load_color(self, scan_id, frame_id):
method load_high_res_color (line 569) | def load_high_res_color(self, scan_id, frame_id):
FILE: sr_datasets/generic_mvs_dataset.py
class GenericMVSDataset (line 15) | class GenericMVSDataset(Dataset):
method __init__ (line 43) | def __init__(self,
method __len__ (line 197) | def __len__(self):
method get_sub_folder_dir (line 201) | def get_sub_folder_dir(split):
method get_valid_frame_path (line 205) | def get_valid_frame_path(self, split, scan):
method get_valid_frame_ids (line 211) | def get_valid_frame_ids(self, split, scan, store_computed=True):
method get_color_filepath (line 233) | def get_color_filepath(self, scan_id, frame_id):
method get_high_res_color_filepath (line 249) | def get_high_res_color_filepath(self, scan_id, frame_id):
method get_cached_depth_filepath (line 266) | def get_cached_depth_filepath(self, scan_id, frame_id):
method get_full_res_depth_filepath (line 281) | def get_full_res_depth_filepath(self, scan_id, frame_id):
method get_pose_filepath (line 297) | def get_pose_filepath(self, scan_id, frame_id):
method get_frame_id_string (line 310) | def get_frame_id_string(self, frame_id):
method get_gt_mesh_path (line 319) | def get_gt_mesh_path(dataset_path, split, scan_id):
method load_intrinsics (line 325) | def load_intrinsics(self, scan_id, frame_id=None, flip=None):
method load_target_size_depth_and_mask (line 347) | def load_target_size_depth_and_mask(self, scan_id, frame_id):
method load_full_res_depth_and_mask (line 368) | def load_full_res_depth_and_mask(self, scan_id, frame_id):
method load_pose (line 386) | def load_pose(self, scan_id, frame_id):
method load_color (line 402) | def load_color(self, scan_id, frame_id):
method load_high_res_color (line 425) | def load_high_res_color(self, scan_id, frame_id):
method get_frame (line 451) | def get_frame(self, scan_id, frame_id, load_depth, flip=False):
method stack_src_data (line 587) | def stack_src_data(self, src_data):
method __getitem__ (line 602) | def __getitem__(self, idx):
FILE: sr_datasets/scannet_dataset.py
class ScannetDataset (line 11) | class ScannetDataset(GenericMVSDataset):
method __init__ (line 77) | def __init__(
method get_sub_folder_dir (line 165) | def get_sub_folder_dir(split):
method get_frame_id_string (line 172) | def get_frame_id_string(self, frame_id):
method get_valid_frame_path (line 181) | def get_valid_frame_path(self, split, scan):
method get_valid_frame_ids (line 190) | def get_valid_frame_ids(self, split, scan, store_computed=True):
method get_gt_mesh_path (line 292) | def get_gt_mesh_path(dataset_path, split, scan_id):
method get_color_filepath (line 304) | def get_color_filepath(self, scan_id, frame_id):
method get_high_res_color_filepath (line 330) | def get_high_res_color_filepath(self, scan_id, frame_id):
method get_cached_depth_filepath (line 357) | def get_cached_depth_filepath(self, scan_id, frame_id):
method get_full_res_depth_filepath (line 379) | def get_full_res_depth_filepath(self, scan_id, frame_id):
method get_pose_filepath (line 399) | def get_pose_filepath(self, scan_id, frame_id):
method load_intrinsics (line 416) | def load_intrinsics(self, scan_id, frame_id=None, flip=False):
method load_target_size_depth_and_mask (line 474) | def load_target_size_depth_and_mask(self, scan_id, frame_id):
method load_full_res_depth_and_mask (line 517) | def load_full_res_depth_and_mask(self, scan_id, frame_id):
method load_pose (line 549) | def load_pose(self, scan_id, frame_id):
FILE: sr_datasets/scanniverse_dataset.py
class ScanniverseDataset (line 14) | class ScanniverseDataset(GenericMVSDataset):
method __init__ (line 27) | def __init__(
method get_sub_folder_dir (line 128) | def get_sub_folder_dir(split):
method load_capture_metadata (line 131) | def load_capture_metadata(self, scan_id):
method get_frame_id_string (line 239) | def get_frame_id_string(self, frame_id):
method get_valid_frame_path (line 248) | def get_valid_frame_path(self, split, scan):
method get_valid_frame_ids (line 256) | def get_valid_frame_ids(self, split, scan, store_computed=True):
method get_color_filepath (line 331) | def get_color_filepath(self, scan_id, frame_id):
method get_high_res_color_filepath (line 369) | def get_high_res_color_filepath(self, scan_id, frame_id):
method get_cached_depth_filepath (line 387) | def get_cached_depth_filepath(self, scan_id, frame_id):
method get_full_res_depth_filepath (line 397) | def get_full_res_depth_filepath(self, scan_id, frame_id):
method load_pose (line 405) | def load_pose(self, scan_id, frame_id):
method load_intrinsics (line 447) | def load_intrinsics(self, scan_id, frame_id, flip=None):
method load_target_size_depth_and_mask (line 514) | def load_target_size_depth_and_mask(self, scan_id, frame_id):
method load_full_res_depth_and_mask (line 528) | def load_full_res_depth_and_mask(self, scan_id, frame_id):
FILE: sr_datasets/seven_scenes_dataset.py
class SevenScenesDataset (line 12) | class SevenScenesDataset(GenericMVSDataset):
method __init__ (line 47) | def __init__(
method get_sub_folder_dir (line 135) | def get_sub_folder_dir(split):
method get_frame_id_string (line 139) | def get_frame_id_string(self, frame_id):
method get_valid_frame_path (line 148) | def get_valid_frame_path(self, split, scan):
method get_valid_frame_ids (line 157) | def get_valid_frame_ids(self, split, scan, store_computed=True):
method get_color_filepath (line 254) | def get_color_filepath(self, scan_id, frame_id):
method get_high_res_color_filepath (line 281) | def get_high_res_color_filepath(self, scan_id, frame_id):
method get_cached_depth_filepath (line 308) | def get_cached_depth_filepath(self, scan_id, frame_id):
method get_full_res_depth_filepath (line 328) | def get_full_res_depth_filepath(self, scan_id, frame_id):
method get_pose_filepath (line 347) | def get_pose_filepath(self, scan_id, frame_id):
method load_intrinsics (line 362) | def load_intrinsics(self, scan_id=None, frame_id=None, flip=None):
method load_target_size_depth_and_mask (line 410) | def load_target_size_depth_and_mask(self, scan_id, frame_id):
method load_full_res_depth_and_mask (line 452) | def load_full_res_depth_and_mask(self, scan_id, frame_id):
method load_pose (line 485) | def load_pose(self, scan_id, frame_id):
FILE: sr_datasets/vdr_dataset.py
class VDRDataset (line 17) | class VDRDataset(GenericMVSDataset):
method __init__ (line 29) | def __init__(
method get_sub_folder_dir (line 78) | def get_sub_folder_dir(split):
method get_frame_id_string (line 81) | def get_frame_id_string(self, frame_id):
method get_valid_frame_path (line 90) | def get_valid_frame_path(self, split, scan):
method get_valid_frame_ids (line 98) | def get_valid_frame_ids(self, split, scan, store_computed=True):
method load_pose (line 168) | def load_pose(self, scan_id, frame_id):
method load_intrinsics (line 207) | def load_intrinsics(self, scan_id, frame_id, flip=None):
method load_capture_metadata (line 266) | def load_capture_metadata(self, scan_id):
method get_cached_depth_filepath (line 294) | def get_cached_depth_filepath(self, scan_id, frame_id):
method get_cached_confidence_filepath (line 327) | def get_cached_confidence_filepath(self, scan_id, frame_id):
method get_full_res_depth_filepath (line 361) | def get_full_res_depth_filepath(self, scan_id, frame_id):
method get_full_res_confidence_filepath (line 383) | def get_full_res_confidence_filepath(self, scan_id, frame_id):
method load_full_res_depth_and_mask (line 404) | def load_full_res_depth_and_mask(self, scan_id, frame_id):
method load_target_size_depth_and_mask (line 444) | def load_target_size_depth_and_mask(self, scan_id, frame_id):
method get_color_filepath (line 513) | def get_color_filepath(self, scan_id, frame_id):
method get_high_res_color_filepath (line 541) | def get_high_res_color_filepath(self, scan_id, frame_id):
FILE: test.py
function main (line 128) | def main(opts):
FILE: tools/fusers_helper.py
class DepthFuser (line 11) | class DepthFuser():
method __init__ (line 12) | def __init__(
class OurFuser (line 22) | class OurFuser(DepthFuser):
method __init__ (line 34) | def __init__(
method fuse_frames (line 64) | def fuse_frames(self, depths_b1hw, K_b44,
method export_mesh (line 73) | def export_mesh(self, path, export_single_mesh=True):
method get_mesh (line 80) | def get_mesh(self, export_single_mesh=True, convert_to_trimesh=True):
class Open3DFuser (line 84) | class Open3DFuser(DepthFuser):
method __init__ (line 90) | def __init__(
method fuse_frames (line 116) | def fuse_frames(
method export_mesh (line 177) | def export_mesh(self, path, use_marching_cubes_mask=None):
method get_mesh (line 180) | def get_mesh(self, export_single_mesh=None, convert_to_trimesh=False):
function get_fuser (line 188) | def get_fuser(opts, scan):
FILE: tools/keyframe_buffer.py
class DVMVS_Config (line 12) | class DVMVS_Config:
function is_pose_available (line 24) | def is_pose_available(pose):
function is_valid_pair (line 33) | def is_valid_pair(
function pose_distance (line 54) | def pose_distance(reference_pose, measurement_pose):
class KeyframeBuffer (line 72) | class KeyframeBuffer:
method __init__ (line 73) | def __init__(
method calculate_penalty (line 89) | def calculate_penalty(self, t_score, R_score):
method try_new_keyframe (line 99) | def try_new_keyframe(self, pose, image, dist_to_last_valid=None, index...
method get_best_measurement_frames (line 163) | def get_best_measurement_frames(self, n_requested_measurement_frames):
class SimpleBuffer (line 189) | class SimpleBuffer:
method __init__ (line 190) | def __init__(
method try_new_keyframe (line 200) | def try_new_keyframe(self, pose, image, index=None):
method get_measurement_frames (line 241) | def get_measurement_frames(self):
class OfflineKeyframeBuffer (line 245) | class OfflineKeyframeBuffer:
method __init__ (line 246) | def __init__(
method calculate_penalty (line 263) | def calculate_penalty(self, t_score, R_score):
method try_new_keyframe (line 273) | def try_new_keyframe(self, pose, image, index=None):
method get_best_measurement_frames (line 332) | def get_best_measurement_frames(self, n_requested_measurement_frames):
method get_best_measurement_frames_for_0index (line 357) | def get_best_measurement_frames_for_0index(self, n_requested_measureme...
FILE: tools/mesh_renderer.py
class Renderer (line 26) | class Renderer():
method __init__ (line 32) | def __init__(self, height=480, width=640, flat_render=False):
method render (line 37) | def render(self, height, width,
method __call__ (line 66) | def __call__(self, height, width, intrinsics, pose, meshes):
method fix_pose (line 69) | def fix_pose(self, pose):
method mesh_opengl (line 81) | def mesh_opengl(self, mesh, mesh_material=None):
method delete (line 87) | def delete(self):
method render_mesh (line 90) | def render_mesh(
method render_mesh_cull_composite (line 134) | def render_mesh_cull_composite(self, alpha, **kwargs):
function render_colour (line 143) | def render_colour(renderer, meshes, world_T_cam, K, height=256, width=320):
class SmoothBirdsEyeCamera (line 155) | class SmoothBirdsEyeCamera():
method __init__ (line 160) | def __init__(
method get_bird_eye_trans (line 178) | def get_bird_eye_trans(self,
function camera_marker (line 265) | def camera_marker(
function get_image_box (line 428) | def get_image_box(
function create_light_array (line 496) | def create_light_array(light_type, center_loc,
function transform_trimesh (line 523) | def transform_trimesh(mesh, transform):
FILE: tools/torch_point_cloud_fusion.py
function process_depth (line 12) | def process_depth(ref_depth, ref_image, src_depths, src_images, ref_P, s...
function process_scene (line 100) | def process_scene(depth_preds, images, poses, K, z_thresh, n_consistent_...
FILE: tools/tsdf.py
class TSDF (line 11) | class TSDF:
method __init__ (line 19) | def __init__(
method from_file (line 37) | def from_file(cls, tsdf_file):
method from_mesh (line 52) | def from_mesh(cls, mesh: trimesh.Trimesh, voxel_size: float):
method from_bounds (line 70) | def from_bounds(cls, bounds: dict, voxel_size: float):
method generate_voxel_coords (line 98) | def generate_voxel_coords(cls,
method cuda (line 111) | def cuda(self):
method cpu (line 118) | def cpu(self):
method to_mesh (line 125) | def to_mesh(self, scale_to_world=True, export_single_mesh=False):
method save (line 159) | def save(self, savepath, filename, save_mesh=True):
class TSDFFuser (line 171) | class TSDFFuser:
method __init__ (line 175) | def __init__(self, tsdf, min_depth=0.5, max_depth=5.0, use_gpu=True):
method voxel_coords (line 198) | def voxel_coords(self):
method tsdf_values (line 202) | def tsdf_values(self):
method tsdf_weights (line 206) | def tsdf_weights(self):
method voxel_size (line 210) | def voxel_size(self):
method shape (line 214) | def shape(self):
method truncation (line 218) | def truncation(self):
method project_to_camera (line 221) | def project_to_camera(self, cam_T_world_T_b44, K_b44):
method integrate_depth (line 238) | def integrate_depth(
FILE: train.py
function main (line 34) | def main(opts):
FILE: utils/dataset_utils.py
function get_dataset (line 8) | def get_dataset(dataset_name,
FILE: utils/generic_utils.py
function copy_code_state (line 15) | def copy_code_state(path):
function readlines (line 36) | def readlines(filepath):
function normalize_depth_single (line 42) | def normalize_depth_single(depth_11hw, mask_11hw, robust=False):
function normalize_depth (line 73) | def normalize_depth(depth_b1hw: torch.Tensor,
function pyrdown (line 88) | def pyrdown(input_tensor: torch.Tensor, num_scales: int = 4):
function upsample (line 96) | def upsample(x):
function batched_trace (line 107) | def batched_trace(mat_bNN):
function tensor_B_to_bM (line 110) | def tensor_B_to_bM(tensor_BS, batch_size, num_views):
function tensor_bM_to_B (line 121) | def tensor_bM_to_B(tensor_bMS):
function combine_dims (line 132) | def combine_dims(x, dim_begin, dim_end):
function to_gpu (line 138) | def to_gpu(input_dict, key_ignores=[]):
function imagenet_normalize (line 147) | def imagenet_normalize(image):
function reverse_imagenet_normalize (line 153) | def reverse_imagenet_normalize(image):
function read_image_file (line 162) | def read_image_file(filepath,
function crop_image_to_target_ratio (line 210) | def crop_image_to_target_ratio(image, target_aspect_ratio=4.0/3.0):
function cache_model_outputs (line 241) | def cache_model_outputs(
FILE: utils/geometry_utils.py
function to_homogeneous (line 12) | def to_homogeneous(input_tensor: Tensor, dim: int = 0) -> Tensor:
class BackprojectDepth (line 22) | class BackprojectDepth(jit.ScriptModule):
method __init__ (line 28) | def __init__(self, height: int, width: int):
method forward (line 51) | def forward(self, depth_b1hw: Tensor, invK_b44: Tensor) -> Tensor:
class Project3D (line 62) | class Project3D(jit.ScriptModule):
method __init__ (line 66) | def __init__(self, eps: float = 1e-8):
method forward (line 72) | def forward(self, points_b4N: Tensor,
class NormalGenerator (line 92) | class NormalGenerator(jit.ScriptModule):
method __init__ (line 93) | def __init__(self, height: int, width: int,
method forward (line 108) | def forward(self, depth_b1hw: Tensor, invK_b44: Tensor) -> Tensor:
function get_angle_dif (line 135) | def get_angle_dif(matA_b33, matB_b33):
function get_camera_rays (line 143) | def get_camera_rays(
function pose_distance (line 178) | def pose_distance(pose_b44):
function qvec2rotmat (line 193) | def qvec2rotmat(qvec):
function rotx (line 213) | def rotx(t):
function roty (line 223) | def roty(t):
function rotz (line 233) | def rotz(t):
FILE: utils/metrics_utils.py
function compute_depth_metrics (line 7) | def compute_depth_metrics(gt, pred, mult_a=False):
function compute_depth_metrics_batched (line 51) | def compute_depth_metrics_batched(gt_bN, pred_bN, valid_masks_bN, mult_a...
class ResultsAverager (line 122) | class ResultsAverager():
method __init__ (line 126) | def __init__(self, exp_name, metrics_name):
method update_results (line 141) | def update_results(self, elem_metrics):
method print_sheets_friendly (line 163) | def print_sheets_friendly(
method output_json (line 200) | def output_json(self, filepath, print_running_metrics=False):
method pretty_print_results (line 236) | def pretty_print_results(
method compute_final_average (line 263) | def compute_final_average(self, ignore_nans=False):
FILE: utils/visualization_utils.py
function colormap_image (line 12) | def colormap_image(
function save_viz_video_frames (line 74) | def save_viz_video_frames(frame_list, path, fps=30):
function quick_viz_export (line 84) | def quick_viz_export(
FILE: visualization_scripts/generate_gt_min_max_cache.py
function main (line 26) | def main(opts):
FILE: visualization_scripts/visualize_scene_depth_output.py
function main (line 34) | def main(opts):
FILE: visualize_live_meshing.py
function main (line 27) | def main(opts):
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[
{
"path": ".gitattributes",
"chars": 63,
"preview": "# GitHub syntax highlighting\npixi.lock linguist-language=YAML\n\n"
},
{
"path": ".gitignore",
"chars": 132,
"preview": "*.pyc\n.vscode/\ndebug_scripts/\nlogs/\nlogs\nmodels/*.ckpt\nweights/*.ckpt\n**__pycache__**\n# pixi environments\n.pixi\ndata/*\nO"
},
{
"path": "LICENSE",
"chars": 8466,
"preview": "Copyright © Niantic, Inc. 2022. Patent Pending.\n\nAll rights reserved.\n\n\n\n==============================================="
},
{
"path": "README.md",
"chars": 32984,
"preview": "# SimpleRecon: 3D Reconstruction Without 3D Convolutions\n\nThis is the reference PyTorch implementation for training and "
},
{
"path": "configs/data/7scenes_default.yaml",
"chars": 345,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/res_nas/shared/datasets/7scenes/\ntuple_info_file_location: data_split"
},
{
"path": "configs/data/7scenes_dense.yaml",
"chars": 349,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/res_nas/shared/datasets/7scenes/\ntuple_info_file_location: data_split"
},
{
"path": "configs/data/colmap_dense.yaml",
"chars": 355,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/res_nas/mohameds/datasets/colmap/\ntuple_info_file_location: /mnt/res_"
},
{
"path": "configs/data/neucon_arkit_default.yaml",
"chars": 303,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/res_nas/mohameds/datasets/arkit\ntuple_info_file_location: data_splits"
},
{
"path": "configs/data/neucon_arkit_dense.yaml",
"chars": 307,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/res_nas/mohameds/datasets/arkit\ntuple_info_file_location: data_splits"
},
{
"path": "configs/data/scannet_default_test.yaml",
"chars": 387,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/scannet-data-png2/\ntuple_info_file_location: data_splits/ScanNetv2/st"
},
{
"path": "configs/data/scannet_default_train.yaml",
"chars": 341,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/scannet-data-png2/\ntuple_info_file_location: data_splits/ScanNetv2/st"
},
{
"path": "configs/data/scannet_default_val.yaml",
"chars": 337,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/scannet-data-png2/\ntuple_info_file_location: data_splits/ScanNetv2/st"
},
{
"path": "configs/data/scannet_dense_test.yaml",
"chars": 343,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/scannet-data-png2/\ntuple_info_file_location: data_splits/ScanNetv2/st"
},
{
"path": "configs/data/scannet_dense_val.yaml",
"chars": 341,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/scannet-data-png2/\ntuple_info_file_location: data_splits/ScanNetv2/st"
},
{
"path": "configs/data/scanniverse_dense.yaml",
"chars": 376,
"preview": "!!python/object:options.Options\ndataset_path: /mnt/res_nas/mohameds/datasets/scanniverse/\ntuple_info_file_location: /mnt"
},
{
"path": "configs/data/vdr_default.yaml",
"chars": 293,
"preview": "!!python/object:options.Options\ndataset_path: /media/data/mosayed/datasets/vdr\ntuple_info_file_location: data_splits/vdr"
},
{
"path": "configs/data/vdr_dense.yaml",
"chars": 297,
"preview": "!!python/object:options.Options\ndataset_path: /media/data/mosayed/datasets/vdr\ntuple_info_file_location: data_splits/vdr"
},
{
"path": "configs/data/vdr_dense_offline.yaml",
"chars": 313,
"preview": "!!python/object:options.Options\ndataset_path: /media/data/mosayed/datasets/vdr\ntuple_info_file_location: data_splits/vdr"
},
{
"path": "configs/data/vdr_dense_rerun.yaml",
"chars": 273,
"preview": "!!python/object:options.Options\ndataset_path: data/vdr\ntuple_info_file_location: data_splits/vdr/\ndataset_scan_split_fil"
},
{
"path": "configs/models/dot_product_model.yaml",
"chars": 402,
"preview": "!!python/object:options.Options\nfeature_volume_type: simple_cost_volume\nbatch_size: 16\ncost_volume_aggregation: dot\ncv_e"
},
{
"path": "configs/models/hero_model.yaml",
"chars": 395,
"preview": "!!python/object:options.Options\nfeature_volume_type: mlp_feature_volume\nbatch_size: 16\ncost_volume_aggregation: dot\ncv_e"
},
{
"path": "data_scripts/7scenes_preprocessing.py",
"chars": 4763,
"preview": "# Code from https://github.com/tsattler/visloc_pseudo_gt_limitations/\n\nimport os\nimport warnings\n\nimport numpy as np\nfro"
},
{
"path": "data_scripts/IOS_LOGGER_ARKIT_README.md",
"chars": 2414,
"preview": "# Running with NeuralRecon's demo data and data from ios-logger.\n\nDownload the demo scene out of ios-logger from here: h"
},
{
"path": "data_scripts/generate_test_tuples.py",
"chars": 22724,
"preview": "\n\"\"\"Script for generating DeeoVideoMVS multiview lists in the split folder \n indicated. It will export these frame tu"
},
{
"path": "data_scripts/generate_train_tuples.py",
"chars": 17353,
"preview": "\"\"\"Script for generating DeeoVideoMVS multiview lists in the split folder \n indicated. It will export these frame tup"
},
{
"path": "data_scripts/ios_logger_preprocessing.py",
"chars": 849,
"preview": "import os\nimport sys\nsys.path.append(\"/\".join(sys.path[0].split(\"/\")[:-1]))\nfrom datasets.arkit_dataset import process_d"
},
{
"path": "data_scripts/precompute_valid_frames.py",
"chars": 5619,
"preview": "\n\"\"\"Script for precomputing and storing a list of valid frames per scan. A valid \n frame is defined as one that has a"
},
{
"path": "data_scripts/scannet_wrangling_scripts/LICENSE",
"chars": 1192,
"preview": "The LICENSE applies only to reader.py and SensorData.py.\n\nCopyright 2017 \nAngela Dai, Angel X. Chang, Manolis Savva, Mac"
},
{
"path": "data_scripts/scannet_wrangling_scripts/README.md",
"chars": 4352,
"preview": "# Downloading and Extracting ScanNetv2\n\n\nDeveloped and tested with python 3.9.\n\nThe included license at LICENSE applies "
},
{
"path": "data_scripts/scannet_wrangling_scripts/SensorData.py",
"chars": 6516,
"preview": "\nimport os, struct\nimport numpy as np\nimport zlib\nimport imageio\nimport cv2\nimport png\nfrom PIL import Image\nfrom contex"
},
{
"path": "data_scripts/scannet_wrangling_scripts/download_scannet.py",
"chars": 12447,
"preview": "#!/usr/bin/env python\n# Downloads ScanNet public data release\n# Run with ./download-scannet.py (or python download-scann"
},
{
"path": "data_scripts/scannet_wrangling_scripts/env.yml",
"chars": 147,
"preview": "name: scannet_extraction\ndependencies:\n - python=3.9.7\n - numpy\n - imageio\n - pillow\n - tqdm\n - pip\n - pip:\n -"
},
{
"path": "data_scripts/scannet_wrangling_scripts/reader.py",
"chars": 3258,
"preview": "import argparse\nfrom concurrent.futures import process\nimport os, sys\nfrom tqdm import tqdm\nfrom multiprocessing.pool im"
},
{
"path": "data_scripts/scannet_wrangling_scripts/splits/scannetv2_test.txt",
"chars": 1300,
"preview": "scene0707_00\nscene0708_00\nscene0709_00\nscene0710_00\nscene0711_00\nscene0712_00\nscene0713_00\nscene0714_00\nscene0715_00\nsce"
},
{
"path": "data_scripts/scannet_wrangling_scripts/splits/scannetv2_train.txt",
"chars": 15613,
"preview": "scene0191_00\nscene0191_01\nscene0191_02\nscene0119_00\nscene0230_00\nscene0528_00\nscene0528_01\nscene0705_00\nscene0705_01\nsce"
},
{
"path": "data_scripts/scannet_wrangling_scripts/splits/scannetv2_val.txt",
"chars": 4056,
"preview": "scene0568_00\nscene0568_01\nscene0568_02\nscene0304_00\nscene0488_00\nscene0488_01\nscene0412_00\nscene0412_01\nscene0217_00\nsce"
},
{
"path": "data_splits/7Scenes/dvmvs_split/dvmvs_test_split.txt",
"chars": 185,
"preview": "redkitchen/seq-01\nredkitchen/seq-07\nchess/seq-01\nchess/seq-02\nheads/seq-02\nfire/seq-01\nfire/seq-02\noffice/seq-01\noffice/"
},
{
"path": "data_splits/7Scenes/dvmvs_split/test_eight_view_deepvmvs.txt",
"chars": 94671,
"preview": "stairs/seq-06 000018 000000 000002 000010 000007 000015 000012 000005\nstairs/seq-06 000029 000000 000018 000025 000007 0"
},
{
"path": "data_splits/ScanNetv2/dvmvs_split/dvmvs_train.txt",
"chars": 18083,
"preview": "scene0031_00\nscene0031_01\nscene0031_02\nscene0070_00\nscene0547_00\nscene0547_01\nscene0547_02\nscene0512_00\nscene0332_00\nsce"
},
{
"path": "data_splits/ScanNetv2/dvmvs_split/dvmvs_val.txt",
"chars": 1586,
"preview": "scene0690_00\nscene0690_01\nscene0453_00\nscene0453_01\nscene0603_00\nscene0603_01\nscene0672_00\nscene0672_01\nscene0364_00\nsce"
},
{
"path": "data_splits/ScanNetv2/dvmvs_split/test_eight_view_deepvmvs.txt",
"chars": 94671,
"preview": "stairs/seq-06 000018 000000 000003 000001 000016 000015 000007 000014\nstairs/seq-06 000029 000000 000018 000003 000008 0"
},
{
"path": "data_splits/ScanNetv2/dvmvs_split/test_eight_view_deepvmvs_dense.txt",
"chars": 842087,
"preview": "stairs/seq-06 000001 000000 000000 000000 000000 000000 000000 000000\nstairs/seq-06 000002 000000 000001 000000 000000 0"
},
{
"path": "data_splits/ScanNetv2/standard_split/scannetv2_test.txt",
"chars": 1300,
"preview": "scene0707_00\nscene0708_00\nscene0709_00\nscene0710_00\nscene0711_00\nscene0712_00\nscene0713_00\nscene0714_00\nscene0715_00\nsce"
},
{
"path": "data_splits/ScanNetv2/standard_split/scannetv2_train.txt",
"chars": 15613,
"preview": "scene0191_00\nscene0191_01\nscene0191_02\nscene0119_00\nscene0230_00\nscene0528_00\nscene0528_01\nscene0705_00\nscene0705_01\nsce"
},
{
"path": "data_splits/ScanNetv2/standard_split/scannetv2_val.txt",
"chars": 4056,
"preview": "scene0568_00\nscene0568_01\nscene0568_02\nscene0304_00\nscene0488_00\nscene0488_01\nscene0412_00\nscene0412_01\nscene0217_00\nsce"
},
{
"path": "data_splits/ScanNetv2/standard_split/test_eight_view_deepvmvs.txt",
"chars": 1765710,
"preview": "scene0718_00 000009 000000 000008 000004 000002 000003 000006 000007\nscene0718_00 000018 000000 000009 000008 000004 000"
},
{
"path": "data_splits/ScanNetv2/standard_split/test_eight_view_deepvmvs_offline.txt",
"chars": 1766331,
"preview": "scene0718_00 000009 000042 000000 000028 000050 000056 000061 000018\nscene0718_00 000018 000042 000028 000000 000050 000"
},
{
"path": "data_splits/ScanNetv2/standard_split/val_eight_view_deepvmvs.txt",
"chars": 6988665,
"preview": "scene0423_02 000417 000427 000436 000443 000454 000465 000474 000481\nscene0663_00 000501 000532 000580 000595 000613 000"
},
{
"path": "data_splits/arkit/scans.txt",
"chars": 20,
"preview": "neucon_demodata_b5f1"
},
{
"path": "data_splits/arkit/test_eight_view_deepvmvs.txt",
"chars": 87147,
"preview": "neucon_demodata_b5f1 00005 00000 00004 00001 00002 00003 00004 00001\nneucon_demodata_b5f1 00010 00000 00005 00004 00009 "
},
{
"path": "data_splits/arkit/test_eight_view_deepvmvs_dense.txt",
"chars": 365700,
"preview": "neucon_demodata_b5f1 00001 00000 00000 00000 00000 00000 00000 00000\nneucon_demodata_b5f1 00002 00000 00001 00001 00000 "
},
{
"path": "data_splits/vdr/scans.txt",
"chars": 17,
"preview": "living_room\nhouse"
},
{
"path": "data_splits/vdr/test_eight_view_deepvmvs.txt",
"chars": 33208,
"preview": "house 12 0 3 6 7 5 2 11\nhouse 17 0 12 10 3 13 11 1\nhouse 21 12 17 0 16 4 11 3\nhouse 25 21 12 17 0 1 16 3\nhouse 29 25 12 "
},
{
"path": "data_splits/vdr/test_eight_view_deepvmvs_dense.txt",
"chars": 93727,
"preview": "living_room 1 0 0 0 0 0 0 0\nliving_room 2 1 0 0 1 1 0 1\nliving_room 3 1 2 0 2 0 0 0\nliving_room 4 1 0 2 3 2 3 1\nliving_r"
},
{
"path": "data_splits/vdr/test_eight_view_deepvmvs_dense_offline.txt",
"chars": 94398,
"preview": "living_room 0 20 17 23 26 30 33 35\nliving_room 1 20 17 23 26 30 33 35\nliving_room 2 20 17 23 26 30 33 35\nliving_room 3 2"
},
{
"path": "experiment_modules/depth_model.py",
"chars": 30690,
"preview": "import logging\n\nimport pytorch_lightning as pl\nimport timm\nimport torch\nimport torch.nn.functional as F\nfrom losses impo"
},
{
"path": "losses.py",
"chars": 8068,
"preview": "import kornia\nimport torch\nimport torch.jit as jit\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\nfrom ut"
},
{
"path": "modules/cost_volume.py",
"chars": 52342,
"preview": "import einops\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import Tensor\n\nfrom modules."
},
{
"path": "modules/layers.py",
"chars": 4167,
"preview": "from typing import Callable, Optional\n\nimport torch.nn as nn\nfrom torch import Tensor\n\n\ndef conv3x3(\n in_plan"
},
{
"path": "modules/networks.py",
"chars": 7974,
"preview": "import antialiased_cnns\nfrom torchvision import models\nimport numpy as np\nimport timm\nimport torch\nfrom torch import nn\n"
},
{
"path": "options.py",
"chars": 15219,
"preview": "import argparse\nimport dataclasses\nimport os\nfrom dataclasses import dataclass\n\nimport yaml\n\n\n@dataclass\nclass Options()"
},
{
"path": "pc_fusion.py",
"chars": 7725,
"preview": "\"\"\" \n Fuses depth maps into point clouds using the PC fuser from \n https://github.com/alexrich021/3dvnet/blob/main"
},
{
"path": "pixi.toml",
"chars": 2525,
"preview": "[project]\nname = \"simplerecon\"\nversion = \"0.1.0\"\ndescription = \"Add a short description here\"\nauthors = [\"Pablo Vela <pa"
},
{
"path": "requirements.txt",
"chars": 307,
"preview": "torch==1.13.1\ntorchvision==0.14.1\nkornia==0.6.7\nantialiased-cnns\nefficientnet_pytorch\ntimm\ntrimesh\ntransforms3d\neinops\nm"
},
{
"path": "rerun_visualize_live_meshing.py",
"chars": 16199,
"preview": "import os\nimport pickle\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport"
},
{
"path": "simplerecon_env.yml",
"chars": 907,
"preview": "name: simplerecon\nchannels:\n - default\n - pytorch\n - conda-forge\ndependencies:\n - python=3.9.7\n - pytorch=1.10.0\n "
},
{
"path": "sr_datasets/arkit_dataset.py",
"chars": 26315,
"preview": "import json\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom sr_datasets.generic_mvs_dataset import Generi"
},
{
"path": "sr_datasets/colmap_dataset.py",
"chars": 22966,
"preview": "import functools\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom sr_datasets.generic_mvs_dataset import G"
},
{
"path": "sr_datasets/generic_mvs_dataset.py",
"chars": 26570,
"preview": "import logging\nimport os\nimport random\n\nimport numpy as np\nimport PIL.Image as pil\nimport torch\nfrom torch.utils.data im"
},
{
"path": "sr_datasets/scannet_dataset.py",
"chars": 23141,
"preview": "\nimport os\nimport numpy as np\nimport PIL.Image as pil\nimport torch\nfrom sr_datasets.generic_mvs_dataset import GenericMV"
},
{
"path": "sr_datasets/scanniverse_dataset.py",
"chars": 22134,
"preview": "import logging\nimport os\nimport re\nimport numpy as np\nimport PIL.Image as pil\nimport torch\nfrom scipy.spatial.transform "
},
{
"path": "sr_datasets/seven_scenes_dataset.py",
"chars": 20318,
"preview": "from doctest import debug_script\nfrom genericpath import exists\nimport os\nimport numpy as np\nimport PIL.Image as pil\nimp"
},
{
"path": "sr_datasets/vdr_dataset.py",
"chars": 22084,
"preview": "import functools\nimport json\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nf"
},
{
"path": "test.py",
"chars": 21212,
"preview": "\"\"\" \n Predicts depth maps using a DepthModel model. Uses an MVS dataset from \n datasets.\n \n All results will"
},
{
"path": "tools/fusers_helper.py",
"chars": 7897,
"preview": "import numpy as np\nimport open3d as o3d\nimport torch\nimport trimesh\nfrom sr_datasets.scannet_dataset import ScannetDatas"
},
{
"path": "tools/keyframe_buffer.py",
"chars": 15064,
"preview": "\"\"\"\nMost of this is a modified version of code from the DeepVideoMVS repository \nat https://github.com/ardaduz/deep-vide"
},
{
"path": "tools/mesh_renderer.py",
"chars": 18727,
"preview": "import os\n\nos.environ[\"PYOPENGL_PLATFORM\"] = \"egl\"\n\nimport sys\nimport numpy as np\nimport pyrender\nimport trimesh\nimport "
},
{
"path": "tools/torch_point_cloud_fusion.py",
"chars": 4974,
"preview": "\"\"\"\n This is borrowed from https://github.com/alexrich021/3dvnet/blob/main/mv3d/eval/pointcloudfusion_custom.py\n\"\"\"\n\n"
},
{
"path": "tools/tsdf.py",
"chars": 12328,
"preview": "import os\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as TF\nimport trimesh\nfrom"
},
{
"path": "train.py",
"chars": 6174,
"preview": "\"\"\" \n Trains a DepthModel model. Uses an MVS dataset from datasets.\n\n - Outputs logs and checkpoints to opts.log_d"
},
{
"path": "utils/dataset_utils.py",
"chars": 4794,
"preview": "from sr_datasets.colmap_dataset import ColmapDataset\nfrom sr_datasets.arkit_dataset import ARKitDataset\nfrom sr_datasets"
},
{
"path": "utils/generic_utils.py",
"chars": 9641,
"preview": "import logging\nimport os\nimport pickle\nfrom pathlib import Path\n\nimport kornia\nimport torch\nimport torchvision.transform"
},
{
"path": "utils/geometry_utils.py",
"chars": 7872,
"preview": "import kornia\nimport numpy as np\nimport torch\nimport torch.jit as jit\nimport torch.nn.functional as F\nfrom torch import "
},
{
"path": "utils/metrics_utils.py",
"chars": 9568,
"preview": "import json\n\nimport numpy as np\nimport torch\n\n\ndef compute_depth_metrics(gt, pred, mult_a=False):\n \"\"\"\n Computes e"
},
{
"path": "utils/visualization_utils.py",
"chars": 6260,
"preview": "import os\n\nimport matplotlib.pyplot as plt\nimport moviepy.editor as mpy\nimport numpy as np\nimport torch\nfrom PIL import "
},
{
"path": "visualization_scripts/generate_gt_min_max_cache.py",
"chars": 4462,
"preview": "\"\"\" \n Loops through an MVS dataset, and extracts smoothed max and min values \n across a scene. Saves those values "
},
{
"path": "visualization_scripts/load_meshes_and_include_normals.py",
"chars": 4075,
"preview": "\"\"\" \n Reads plys defined with pattern in scans_path_pattern. First computes \n normals for each scan using open3d, "
},
{
"path": "visualization_scripts/visualize_scene_depth_output.py",
"chars": 15614,
"preview": "\"\"\" \n Loops through precomputed saved depth maps and associated RGB and groundtruth images, \n and generates side b"
},
{
"path": "visualize_live_meshing.py",
"chars": 18120,
"preview": "import os\nimport pickle\nfrom pathlib import Path\n\nimport numpy as np\nimport pyrender\nimport torch\nimport torch.nn.functi"
},
{
"path": "weights/strip_checkpoint.py",
"chars": 737,
"preview": "# for importing options on checkpoint load.\nimport sys\nsys.path.append(\"/\".join(sys.path[0].split(\"/\")[:-1])) \n\nimport t"
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the rerun-io/simplerecon GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 92 files (49.9 MB), approximately 3.2M tokens, and a symbol index with 370 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.