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Repository: bmild/nerf
Branch: master
Commit: 14c55567a6d0
Files: 20
Total size: 4.2 MB
Directory structure:
gitextract_x9b67lj8/
├── .gitignore
├── LICENSE
├── README.md
├── config_deepvoxels_greek.txt
├── config_fern.txt
├── config_lego.txt
├── download_example_data.sh
├── download_example_weights.sh
├── environment.yml
├── extract_mesh.ipynb
├── load_blender.py
├── load_deepvoxels.py
├── load_llff.py
├── paper_configs/
│ ├── blender_config.txt
│ ├── deepvoxels_config.txt
│ └── llff_config.txt
├── render_demo.ipynb
├── run_nerf.py
├── run_nerf_helpers.py
└── tiny_nerf.ipynb
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
**/.ipynb_checkpoints
**/__pycache__
*.png
*.mp4
*.npy
*.npz
*.dae
data/*
logs/*
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2020 bmild
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: README.md
================================================
# NeRF: Neural Radiance Fields
### [Project Page](http://matthewtancik.com/nerf) | [Video](https://youtu.be/JuH79E8rdKc) | [Paper](https://arxiv.org/abs/2003.08934) | [Data](https://drive.google.com/drive/folders/1cK3UDIJqKAAm7zyrxRYVFJ0BRMgrwhh4)
[](https://colab.research.google.com/github/bmild/nerf/blob/master/tiny_nerf.ipynb)<br>
Tensorflow implementation of optimizing a neural representation for a single scene and rendering new views.<br><br>
[NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](http://tancik.com/nerf)
[Ben Mildenhall](https://bmild.github.io/)\*<sup>1</sup>,
[Pratul P. Srinivasan](https://people.eecs.berkeley.edu/~pratul/)\*<sup>1</sup>,
[Matthew Tancik](http://matthewtancik.com/)\*<sup>1</sup>,
[Jonathan T. Barron](http://jonbarron.info/)<sup>2</sup>,
[Ravi Ramamoorthi](http://cseweb.ucsd.edu/~ravir/)<sup>3</sup>,
[Ren Ng](https://www2.eecs.berkeley.edu/Faculty/Homepages/yirenng.html)<sup>1</sup> <br>
<sup>1</sup>UC Berkeley, <sup>2</sup>Google Research, <sup>3</sup>UC San Diego
\*denotes equal contribution
in ECCV 2020 (Oral Presentation, Best Paper Honorable Mention)
<img src='imgs/pipeline.jpg'/>
## TL;DR quickstart
To setup a conda environment, download example training data, begin the training process, and launch Tensorboard:
```
conda env create -f environment.yml
conda activate nerf
bash download_example_data.sh
python run_nerf.py --config config_fern.txt
tensorboard --logdir=logs/summaries --port=6006
```
If everything works without errors, you can now go to `localhost:6006` in your browser and watch the "Fern" scene train.
## Setup
Python 3 dependencies:
* Tensorflow 1.15
* matplotlib
* numpy
* imageio
* configargparse
The LLFF data loader requires ImageMagick.
We provide a conda environment setup file including all of the above dependencies. Create the conda environment `nerf` by running:
```
conda env create -f environment.yml
```
You will also need the [LLFF code](http://github.com/fyusion/llff) (and COLMAP) set up to compute poses if you want to run on your own real data.
## What is a NeRF?
A neural radiance field is a simple fully connected network (weights are ~5MB) trained to reproduce input views of a single scene using a rendering loss. The network directly maps from spatial location and viewing direction (5D input) to color and opacity (4D output), acting as the "volume" so we can use volume rendering to differentiably render new views.
Optimizing a NeRF takes between a few hours and a day or two (depending on resolution) and only requires a single GPU. Rendering an image from an optimized NeRF takes somewhere between less than a second and ~30 seconds, again depending on resolution.
## Running code
Here we show how to run our code on two example scenes. You can download the rest of the synthetic and real data used in the paper [here](https://drive.google.com/drive/folders/1cK3UDIJqKAAm7zyrxRYVFJ0BRMgrwhh4).
### Optimizing a NeRF
Run
```
bash download_example_data.sh
```
to get the our synthetic Lego dataset and the LLFF Fern dataset.
To optimize a low-res Fern NeRF:
```
python run_nerf.py --config config_fern.txt
```
After 200k iterations (about 15 hours), you should get a video like this at `logs/fern_test/fern_test_spiral_200000_rgb.mp4`:

To optimize a low-res Lego NeRF:
```
python run_nerf.py --config config_lego.txt
```
After 200k iterations, you should get a video like this:

### Rendering a NeRF
Run
```
bash download_example_weights.sh
```
to get a pretrained high-res NeRF for the Fern dataset. Now you can use [`render_demo.ipynb`](https://github.com/bmild/nerf/blob/master/render_demo.ipynb) to render new views.
### Replicating the paper results
The example config files run at lower resolutions than the quantitative/qualitative results in the paper and video. To replicate the results from the paper, start with the config files in [`paper_configs/`](https://github.com/bmild/nerf/tree/master/paper_configs). Our synthetic Blender data and LLFF scenes are hosted [here](https://drive.google.com/drive/folders/1cK3UDIJqKAAm7zyrxRYVFJ0BRMgrwhh4) and the DeepVoxels data is hosted by Vincent Sitzmann [here](https://drive.google.com/open?id=1lUvJWB6oFtT8EQ_NzBrXnmi25BufxRfl).
### Extracting geometry from a NeRF
Check out [`extract_mesh.ipynb`](https://github.com/bmild/nerf/blob/master/extract_mesh.ipynb) for an example of running marching cubes to extract a triangle mesh from a trained NeRF network. You'll need the install the [PyMCubes](https://github.com/pmneila/PyMCubes) package for marching cubes plus the [trimesh](https://github.com/mikedh/trimesh) and [pyrender](https://github.com/mmatl/pyrender) packages if you want to render the mesh inside the notebook:
```
pip install trimesh pyrender PyMCubes
```
## Generating poses for your own scenes
### Don't have poses?
We recommend using the `imgs2poses.py` script from the [LLFF code](https://github.com/fyusion/llff). Then you can pass the base scene directory into our code using `--datadir <myscene>` along with `-dataset_type llff`. You can take a look at the `config_fern.txt` config file for example settings to use for a forward facing scene. For a spherically captured 360 scene, we recomment adding the `--no_ndc --spherify --lindisp` flags.
### Already have poses!
In `run_nerf.py` and all other code, we use the same pose coordinate system as in OpenGL: the local camera coordinate system of an image is defined in a way that the X axis points to the right, the Y axis upwards, and the Z axis backwards as seen from the image.
Poses are stored as 3x4 numpy arrays that represent camera-to-world transformation matrices. The other data you will need is simple pinhole camera intrinsics (`hwf = [height, width, focal length]`) and near/far scene bounds. Take a look at [our data loading code](https://github.com/bmild/nerf/blob/master/run_nerf.py#L406) to see more.
## Citation
```
@inproceedings{mildenhall2020nerf,
title={NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis},
author={Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng},
year={2020},
booktitle={ECCV},
}
```
================================================
FILE: config_deepvoxels_greek.txt
================================================
expname = dvox_greek_test
basedir = ./logs
# datadir = /your/path/to/deepvoxels/data
dataset_type = deepvoxels
shape = greek
no_batching = True
N_samples = 64
N_importance = 64
use_viewdirs = True
white_bkgd = True
N_rand = 1024
================================================
FILE: config_fern.txt
================================================
expname = fern_test
basedir = ./logs
datadir = ./data/nerf_llff_data/fern
dataset_type = llff
factor = 8
llffhold = 8
N_rand = 1024
N_samples = 64
N_importance = 64
use_viewdirs = True
raw_noise_std = 1e0
================================================
FILE: config_lego.txt
================================================
expname = lego_test
basedir = ./logs
datadir = ./data/nerf_synthetic/lego
dataset_type = blender
half_res = True
no_batching = True
N_samples = 64
N_importance = 64
use_viewdirs = True
white_bkgd = True
N_rand = 1024
================================================
FILE: download_example_data.sh
================================================
wget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/tiny_nerf_data.npz
mkdir -p data
cd data
wget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/nerf_example_data.zip
unzip nerf_example_data.zip
cd ..
================================================
FILE: download_example_weights.sh
================================================
mkdir -p logs
cd logs
wget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/fern_example_weights.zip
unzip fern_example_weights.zip
wget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/lego_example_weights.zip
unzip lego_example_weights.zip
================================================
FILE: environment.yml
================================================
# To run: conda env create -f environment.yml
name: nerf
channels:
- conda-forge
dependencies:
- python=3.7
- pip
- cudatoolkit=10.0
- tensorflow-gpu==1.15
- numpy
- matplotlib
- imageio
- imageio-ffmpeg
- configargparse
- imagemagick
================================================
FILE: extract_mesh.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os, sys\n",
"# os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n",
"# os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
"import tensorflow as tf\n",
"tf.compat.v1.enable_eager_execution()\n",
"\n",
"import numpy as np\n",
"import imageio\n",
"import pprint\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import run_nerf\n",
"import run_nerf_helpers\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load trained network weights\n",
"Run `bash download_example_weights.sh` in the root directory if you need to download the Lego example weights"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Args:\n",
"expname = lego_example\n",
"basedir = ./logs\n",
"datadir = ./data/nerf_synthetic/lego\n",
"dataset_type = blender\n",
"\n",
"half_res = True\n",
"no_batching = True\n",
"\n",
"N_samples = 64\n",
"N_importance = 64\n",
"\n",
"use_viewdirs = True\n",
"\n",
"white_bkgd = True\n",
"\n",
"N_rand = 1024\n",
"\n",
"MODEL 63 27 <class 'int'> <class 'int'> True\n",
"(?, 90) (?, 63) (?, 27)\n",
"MODEL 63 27 <class 'int'> <class 'int'> True\n",
"(?, 90) (?, 63) (?, 27)\n",
"Not ndc!\n",
"Found ckpts ['./logs/lego_example/model_200000.npy']\n",
"Reloading from ./logs/lego_example/model_200000.npy\n",
"Resetting step to 200001\n",
"Reloading fine from ./logs/lego_example/model_fine_200000.npy\n",
"Render kwargs:\n",
"{'N_importance': 64,\n",
" 'N_samples': 64,\n",
" 'far': <tf.Tensor: id=1113, shape=(), dtype=float32, numpy=6.0>,\n",
" 'lindisp': False,\n",
" 'ndc': False,\n",
" 'near': <tf.Tensor: id=1112, shape=(), dtype=float32, numpy=2.0>,\n",
" 'network_fine': <tensorflow.python.keras.engine.training.Model object at 0x7f1fe02d1eb8>,\n",
" 'network_fn': <tensorflow.python.keras.engine.training.Model object at 0x7f1fe037d2e8>,\n",
" 'network_query_fn': <function create_nerf.<locals>.<lambda> at 0x7f1ff1636048>,\n",
" 'perturb': False,\n",
" 'raw_noise_std': 0.0,\n",
" 'use_viewdirs': True,\n",
" 'white_bkgd': True}\n",
"<function create_nerf.<locals>.<lambda> at 0x7f1ff1636048>\n",
"WARNING:tensorflow:From /home/jupyter/code_public/run_nerf_helpers.py:181: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"basedir = './logs'\n",
"expname = 'lego_example'\n",
"\n",
"config = os.path.join(basedir, expname, 'config.txt')\n",
"print('Args:')\n",
"print(open(config, 'r').read())\n",
"\n",
"parser = run_nerf.config_parser()\n",
"ft_str = '' \n",
"ft_str = '--ft_path {}'.format(os.path.join(basedir, expname, 'model_200000.npy'))\n",
"args = parser.parse_args('--config {} '.format(config) + ft_str)\n",
"\n",
"# Create nerf model\n",
"_, render_kwargs_test, start, grad_vars, models = run_nerf.create_nerf(args)\n",
"\n",
"bds_dict = {\n",
" 'near' : tf.cast(2., tf.float32),\n",
" 'far' : tf.cast(6., tf.float32),\n",
"}\n",
"render_kwargs_test.update(bds_dict)\n",
"\n",
"print('Render kwargs:')\n",
"pprint.pprint(render_kwargs_test)\n",
"\n",
"net_fn = render_kwargs_test['network_query_fn']\n",
"print(net_fn)\n",
"\n",
"# Render an overhead view to check model was loaded correctly\n",
"c2w = np.eye(4)[:3,:4].astype(np.float32) # identity pose matrix\n",
"c2w[2,-1] = 4.\n",
"H, W, focal = 800, 800, 1200.\n",
"down = 8\n",
"test = run_nerf.render(H//down, W//down, focal/down, c2w=c2w, **render_kwargs_test)\n",
"img = np.clip(test[0],0,1)\n",
"plt.imshow(img)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Query network on dense 3d grid of points"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(257, 257, 257, 3)\n",
"(257, 257, 257, 4)\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N = 256\n",
"t = np.linspace(-1.2, 1.2, N+1)\n",
"\n",
"query_pts = np.stack(np.meshgrid(t, t, t), -1).astype(np.float32)\n",
"print(query_pts.shape)\n",
"sh = query_pts.shape\n",
"flat = query_pts.reshape([-1,3])\n",
"\n",
"\n",
"def batchify(fn, chunk):\n",
" if chunk is None:\n",
" return fn\n",
" def ret(inputs):\n",
" return tf.concat([fn(inputs[i:i+chunk]) for i in range(0, inputs.shape[0], chunk)], 0)\n",
" return ret\n",
" \n",
" \n",
"fn = lambda i0, i1 : net_fn(flat[i0:i1,None,:], viewdirs=np.zeros_like(flat[i0:i1]), network_fn=render_kwargs_test['network_fine'])\n",
"chunk = 1024*64\n",
"raw = np.concatenate([fn(i, i+chunk).numpy() for i in range(0, flat.shape[0], chunk)], 0)\n",
"raw = np.reshape(raw, list(sh[:-1]) + [-1])\n",
"sigma = np.maximum(raw[...,-1], 0.)\n",
"\n",
"print(raw.shape)\n",
"plt.hist(np.maximum(0,sigma.ravel()), log=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Marching cubes with [PyMCubes](https://github.com/pmneila/PyMCubes)\n",
"Change `threshold` to use a different sigma threshold for the isosurface"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fraction occupied 0.03597676833842202\n",
"done (394554, 3) (791052, 3)\n"
]
}
],
"source": [
"import mcubes\n",
"\n",
"threshold = 50.\n",
"print('fraction occupied', np.mean(sigma > threshold))\n",
"vertices, triangles = mcubes.marching_cubes(sigma, threshold)\n",
"print('done', vertices.shape, triangles.shape)\n",
"\n",
"### Uncomment to save out the mesh\n",
"# mcubes.export_mesh(vertices, triangles, \"logs/lego_example/lego_{}.dae\".format(N), \"lego\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Live preview with [trimesh](https://github.com/mikedh/trimesh)\n",
"Click and drag to change viewpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import trimesh\n",
"\n",
"mesh = trimesh.Trimesh(vertices / N - .5, triangles)\n",
"mesh.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save out video with [pyrender](https://github.com/mmatl/pyrender)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"PYOPENGL_PLATFORM\"] = \"egl\"\n",
"import pyrender\n",
"from load_blender import pose_spherical"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"scene = pyrender.Scene()\n",
"scene.add(pyrender.Mesh.from_trimesh(mesh, smooth=False))\n",
"\n",
"# Set up the camera -- z-axis away from the scene, x-axis right, y-axis up\n",
"camera = pyrender.PerspectiveCamera(yfov=np.pi / 3.0)\n",
"\n",
"camera_pose = pose_spherical(-20., -40., 1.).numpy()\n",
"nc = pyrender.Node(camera=camera, matrix=camera_pose)\n",
"scene.add_node(nc)\n",
"\n",
"# Set up the light -- a point light in the same spot as the camera\n",
"light = pyrender.PointLight(color=np.ones(3), intensity=4.0)\n",
"nl = pyrender.Node(light=light, matrix=camera_pose)\n",
"scene.add_node(nl)\n",
"\n",
"# Render the scene\n",
"r = pyrender.OffscreenRenderer(640, 480)\n",
"color, depth = r.render(scene)\n",
"\n",
"plt.imshow(color)\n",
"plt.show()\n",
"plt.imshow(depth)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"done\n"
]
}
],
"source": [
"imgs = []\n",
"for th in np.linspace(0, 360., 120+1)[:-1]:\n",
" camera_pose = pose_spherical(th, -40., 1.).numpy()\n",
" scene.set_pose(nc, pose=camera_pose)\n",
" imgs.append(r.render(scene)[0])\n",
"f = 'logs/lego_example/lego_mesh_turntable.mp4'\n",
"imageio.mimwrite(f, imgs, fps=30)\n",
"print('done')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<video width=400 controls autoplay loop>\n",
" <source 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
gitextract_x9b67lj8/ ├── .gitignore ├── LICENSE ├── README.md ├── config_deepvoxels_greek.txt ├── config_fern.txt ├── config_lego.txt ├── download_example_data.sh ├── download_example_weights.sh ├── environment.yml ├── extract_mesh.ipynb ├── load_blender.py ├── load_deepvoxels.py ├── load_llff.py ├── paper_configs/ │ ├── blender_config.txt │ ├── deepvoxels_config.txt │ └── llff_config.txt ├── render_demo.ipynb ├── run_nerf.py ├── run_nerf_helpers.py └── tiny_nerf.ipynb
SYMBOL INDEX (35 symbols across 5 files)
FILE: load_blender.py
function pose_spherical (line 32) | def pose_spherical(theta, phi, radius):
function load_blender_data (line 41) | def load_blender_data(basedir, half_res=False, testskip=1):
FILE: load_deepvoxels.py
function load_dv_data (line 6) | def load_dv_data(scene='cube', basedir='/data/deepvoxels', testskip=8):
FILE: load_llff.py
function _minify (line 8) | def _minify(basedir, factors=[], resolutions=[]):
function _load_data (line 62) | def _load_data(basedir, factor=None, width=None, height=None, load_imgs=...
function normalize (line 125) | def normalize(x):
function viewmatrix (line 128) | def viewmatrix(z, up, pos):
function ptstocam (line 136) | def ptstocam(pts, c2w):
function poses_avg (line 140) | def poses_avg(poses):
function render_path_spiral (line 153) | def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
function recenter_poses (line 166) | def recenter_poses(poses):
function spherify_poses (line 184) | def spherify_poses(poses, bds):
function load_llff_data (line 243) | def load_llff_data(basedir, factor=8, recenter=True, bd_factor=.75, sphe...
FILE: run_nerf.py
function batchify (line 20) | def batchify(fn, chunk):
function run_network (line 30) | def run_network(inputs, viewdirs, fn, embed_fn, embeddirs_fn, netchunk=1...
function render_rays (line 48) | def render_rays(ray_batch,
function batchify_rays (line 246) | def batchify_rays(rays_flat, chunk=1024*32, **kwargs):
function render (line 260) | def render(H, W, focal,
function render_path (line 339) | def render_path(render_poses, hwf, chunk, render_kwargs, gt_imgs=None, s...
function create_nerf (line 378) | def create_nerf(args):
function config_parser (line 461) | def config_parser():
function train (line 575) | def train():
FILE: run_nerf_helpers.py
function img2mse (line 11) | def img2mse(x, y): return tf.reduce_mean(tf.square(x - y))
function mse2psnr (line 14) | def mse2psnr(x): return -10.*tf.log(x)/tf.log(10.)
function to8b (line 17) | def to8b(x): return (255*np.clip(x, 0, 1)).astype(np.uint8)
class Embedder (line 22) | class Embedder:
method __init__ (line 24) | def __init__(self, **kwargs):
method create_embedding_fn (line 29) | def create_embedding_fn(self):
method embed (line 55) | def embed(self, inputs):
function get_embedder (line 59) | def get_embedder(multires, i=0):
function init_nerf_model (line 80) | def init_nerf_model(D=8, W=256, input_ch=3, input_ch_views=3, output_ch=...
function get_rays (line 123) | def get_rays(H, W, focal, c2w):
function get_rays_np (line 133) | def get_rays_np(H, W, focal, c2w):
function ndc_rays (line 143) | def ndc_rays(H, W, focal, near, rays_o, rays_d):
function sample_pdf (line 183) | def sample_pdf(bins, weights, N_samples, det=False):
Condensed preview — 20 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (4,431K chars).
[
{
"path": ".gitignore",
"chars": 80,
"preview": "**/.ipynb_checkpoints\n**/__pycache__\n*.png\n*.mp4\n*.npy\n*.npz\n*.dae\ndata/*\nlogs/*"
},
{
"path": "LICENSE",
"chars": 1062,
"preview": "MIT License\n\nCopyright (c) 2020 bmild\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof t"
},
{
"path": "README.md",
"chars": 6426,
"preview": "# NeRF: Neural Radiance Fields\n### [Project Page](http://matthewtancik.com/nerf) | [Video](https://youtu.be/JuH79E8rdKc)"
},
{
"path": "config_deepvoxels_greek.txt",
"chars": 233,
"preview": "expname = dvox_greek_test\nbasedir = ./logs\n# datadir = /your/path/to/deepvoxels/data\ndataset_type = deepvoxels\nshape = g"
},
{
"path": "config_fern.txt",
"chars": 209,
"preview": "expname = fern_test\nbasedir = ./logs\ndatadir = ./data/nerf_llff_data/fern\ndataset_type = llff\n\nfactor = 8\nllffhold = 8\n\n"
},
{
"path": "config_lego.txt",
"chars": 221,
"preview": "expname = lego_test\nbasedir = ./logs\ndatadir = ./data/nerf_synthetic/lego\ndataset_type = blender\n\nhalf_res = True\nno_bat"
},
{
"path": "download_example_data.sh",
"chars": 233,
"preview": "wget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/tiny_nerf_data.npz\nmkdir -p data\ncd data\nwget http:/"
},
{
"path": "download_example_weights.sh",
"chars": 270,
"preview": "mkdir -p logs\ncd logs\nwget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/fern_example_weights.zip\nunzip"
},
{
"path": "environment.yml",
"chars": 279,
"preview": "# To run: conda env create -f environment.yml\nname: nerf\nchannels:\n - conda-forge\ndependencies:\n - python=3.7\n "
},
{
"path": "extract_mesh.ipynb",
"chars": 1253115,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "load_blender.py",
"chars": 2396,
"preview": "import os\nimport tensorflow as tf\nimport numpy as np\nimport imageio \nimport json\n\n\n\n\ntrans_t = lambda t : tf.convert_to_"
},
{
"path": "load_deepvoxels.py",
"chars": 3890,
"preview": "import os\nimport numpy as np\nimport imageio \n\n\ndef load_dv_data(scene='cube', basedir='/data/deepvoxels', testskip=8):\n "
},
{
"path": "load_llff.py",
"chars": 9990,
"preview": "import numpy as np\nimport os, imageio\n\n\n########## Slightly modified version of LLFF data loading code \n########## see "
},
{
"path": "paper_configs/blender_config.txt",
"chars": 375,
"preview": "# This replicates the paper result for \"Lego\"\n# when trained to 500k iters. Settings are the \n# same for all other synth"
},
{
"path": "paper_configs/deepvoxels_config.txt",
"chars": 549,
"preview": "# This replicates the paper result for \"Cube\"\n# when trained to 200k iters. Settings are the \n# same for all other DeepV"
},
{
"path": "paper_configs/llff_config.txt",
"chars": 392,
"preview": "# This replicates the paper result for \"Fern\"\n# when trained to 200k iters. Settings are the \n# same for all other LLFF-"
},
{
"path": "render_demo.ipynb",
"chars": 170435,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "run_nerf.py",
"chars": 38002,
"preview": "import os\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n\nimport sys\nimport tensorflow as tf\nimport numpy as np\nimport"
},
{
"path": "run_nerf_helpers.py",
"chars": 6746,
"preview": "import os\nimport sys\nimport tensorflow as tf\nimport numpy as np\nimport imageio\nimport json\n\n\n# Misc utils\n\ndef img2mse(x"
},
{
"path": "tiny_nerf.ipynb",
"chars": 2888714,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"lLDTVWKq7-ei\"\n },\n"
}
]
About this extraction
This page contains the full source code of the bmild/nerf GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 20 files (4.2 MB), approximately 1.1M tokens, and a symbol index with 35 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.