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Repository: ZhiChen902/SC2-PCR
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Commit: 19961eae0a47
Files: 31
Total size: 1.2 MB
Directory structure:
gitextract_aendpbmi/
├── .gitignore
├── 3DLoMatch.pkl
├── LICENSE
├── README.md
├── SC2_PCR.py
├── benchmark_utils.py
├── benchmark_utils_predator.py
├── benchmarks/
│ └── 3DLoMatch/
│ ├── 7-scenes-redkitchen/
│ │ └── gt.info
│ ├── sun3d-home_at-home_at_scan1_2013_jan_1/
│ │ └── gt.info
│ ├── sun3d-home_md-home_md_scan9_2012_sep_30/
│ │ └── gt.info
│ ├── sun3d-hotel_uc-scan3/
│ │ └── gt.info
│ ├── sun3d-hotel_umd-maryland_hotel1/
│ │ └── gt.info
│ ├── sun3d-hotel_umd-maryland_hotel3/
│ │ └── gt.info
│ ├── sun3d-mit_76_studyroom-76-1studyroom2/
│ │ └── gt.info
│ └── sun3d-mit_lab_hj-lab_hj_tea_nov_2_2012_scan1_erika/
│ └── gt.info
├── common.py
├── config.py
├── config_json/
│ ├── config_3DLoMatch.json
│ ├── config_3DMatch.json
│ └── config_KITTI.json
├── dataset.py
├── environment.yml
├── evaluate_metric.py
├── test_3DLoMatch.py
├── test_3DMatch.py
├── test_KITTI.py
└── utils/
├── SE3.py
├── max_clique.py
├── pointcloud.py
├── sinkhorn.py
└── timer.py
================================================
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MIT License
Copyright (c) 2021 ZhiChen
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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FILE: README.md
================================================
# SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration (CVPR 2022)
PyTorch implementation of the paper:
[SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration](https://arxiv.org/abs/2203.14453).
Zhi Chen, [Kun Sun](https://scholar.google.com/citations?user=Ay6kCm4AAAAJ&hl=en), Fan Yang, [Wenbing Tao](https://scholar.google.co.uk/citations?user=jRDPE2AAAAAJ&hl=zh-CN&oi=ao).
## Introduction
In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR. Firstly, we propose a second order spatial compatibility (SC^2) measure to compute the similarity between correspondences. It considers the global compatibility instead of local consistency, allowing for more distinctive clustering between inliers and outliers at early stage. Based on this measure, our registration pipeline employs a global spectral technique to find some reliable seeds from the initial correspondences. Then we design a two-stage strategy to expand each seed to a consensus set based on the SC^2 measure matrix. Finally, we feed each consensus set to a weighted SVD algorithm to generate a candidate rigid transformation and select the best model as the final result. Our method can guarantee to find a certain number of outlier-free consensus sets using fewer samplings, making the model estimation more efficient and robust. In addition, the proposed SC^2 measure is general and can be easily plugged into deep learning based frameworks. Extensive experiments are carried out to investigate the performance of our method.
## Requirements
If you are using conda, you may configure SC2-PCR as:
conda env create -f environment.yml
conda activate SC2_PCR
## 3DMatch
### Data preparation
Downsample and extract FPFH and FCGF descriptors for each frame of the 3DMatch test dataset. [Here](https://drive.google.com/file/d/1kRwuTHlNPr9siENcEMddCO23Oaq0cz-X/view?usp=sharing) we provide the processed test set with pre-computed FPFH/FCGF descriptors. The data should be organized as follows:
```
--data--3DMatch
├── fragments
│ ├── 7-scene-redkitechen/
| | ├── cloud_bin_0.ply
| | ├── cloud_bin_0_fcgf.npz
| | ├── cloud_bin_0_fpfh.npz
│ | └── ...
│ ├── sun3d-home_at-home_at_scan1_2013_jan_1/
│ └── ...
├── gt_result
│ ├── 7-scene-redkitechen-evaluation/
| | ├── 3dmatch.log
| | ├── gt.info
| | ├── gt.log
│ | └── ...
│ ├── sun3d-home_at-home_at_scan1_2013_jan_1-evaluation/
│ └── ...
```
### Testing
Use the following command for testing.
```bash
python ./test_3DMatch.py --config_path config_json/config_3DMatch.json
```
The CUDA_DEVICE and basic parameters can be changed in the json file.
## 3DLoMatch
### Data preparation
FPFH and FCGF descriptors can be prepared in the same way as testing 3DMatch. If you want to test the [predator](https://github.com/prs-eth/OverlapPredator) descriptor, you should first follow the offical instruction of predator to extract the descriptors for 3DMatch dataset and organize the data as follows:
```
--data--3DLoMatch
├── 0.pth
├── 1.pth
├── ...
└── 1780.pth
```
### Testing
Use the following command for testing.
```bash
python ./test_3DLoMatch.py --config_path config_json/config_3DLoMatch.json
```
## KITTI odometry
### Data preparation
Downsample and extract FPFH and FCGF descriptors for each frame of the KITTI test dataset. The raw point clouds can be download from [KITTI Odometry website.](http://www.cvlibs.net/datasets/kitti/eval_odometry.php). For your convenience, [here](https://drive.google.com/drive/folders/1sxkHYjWHhSUE3IcvmZ2p1ziw1LqJqqfc?usp=sharing) we provide the pre-computed FPFH and FCGF descriptors for the KITTI test set.
```
--data--KITTI
├── fpfh_test
│ ├── pair_0.npz
| ├── pair_1.npz
| ├── ...
| └── pair_554.npz
├── fcgf_test
│ ├── pair_0.npz
| ├── pair_1.npz
| ├── ...
| └── pair_554.npz
```
### Testing
Use the following command for testing.
```bash
python ./test_KITTI.py --config_path config_json/config_KITTI.json
```
## Results
### 3DMatch
We evaluate SC^2-PCR on the standard 3DMatch benchmarks:
| Benchmark | RR(%) | RE(°) |TE(cm) | IP(%) | IR(%) | F1(%) |
|:---------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| 3DMatch+FPFH | 83.98 | 2.18 | 6.56 | 72.48 | 78.33 | 75.10 |
| 3DMatch+FCGF | 93.28 | 2.08 | 6.55 | 78.94 | 86.39 | 82.20 |
### 3DMatch
We evaluate SC^2-PCR on the standard 3DLoMatch benchmarks:
| Benchmark | RR(%) | RE(°) |TE(cm) | IP(%) | IR(%) | F1(%) |
|:--------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| 3DLoMatch+FCGF | 57.83 | 3.77 | 10.46 | 44.87 | 53.69 | 48.38 |
| 3DLoMatch+Predator | 69.46 | 3.46 | 9.58 | 56.98 | 67.47 | 61.08 |
### KITTI odometry
We evaluate SC^2-PCR on the standard KITTI benchmarks:
| Benchmark | RR(%) | RE(°) |TE(cm) | IP(%) | IR(%) | F1(%) |
|:---------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| KITTI+FPFH | 99.64 | 0.32 | 7.23 | 93.63 | 95.89 | 94.63 |
| KITTI+FCGF | 98.20 | 0.33 | 20.95 | 82.01 | 91.03 | 85.90 |
## Citation
```bibtex
@InProceedings{Chen_2022_CVPR,
author = {Chen, Zhi and Sun, Kun and Yang, Fan and Tao, Wenbing},
title = {SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {13221-13231}
}
```
## Acknowledgements
- [PointDSC](https://github.com/XuyangBai/PointDSC)
- [FCGF](https://github.com/chrischoy/FCGF)
- [DGR](https://github.com/chrischoy/DeepGlobalRegistration)
- [PREDATOR](https://github.com/prs-eth/OverlapPredator)
================================================
FILE: SC2_PCR.py
================================================
import torch
from common import knn, rigid_transform_3d
from utils.SE3 import transform
import numpy as np
class Matcher():
def __init__(self,
inlier_threshold=0.10,
num_node='all',
use_mutual=True,
d_thre=0.1,
num_iterations=10,
ratio=0.2,
nms_radius=0.1,
max_points=8000,
k1=30,
k2=20,
select_scene=None,
):
self.inlier_threshold = inlier_threshold
self.num_node = num_node
self.use_mutual = use_mutual
self.d_thre = d_thre
self.num_iterations = num_iterations # maximum iteration of power iteration algorithm
self.ratio = ratio # the maximum ratio of seeds.
self.max_points = max_points
self.nms_radius = nms_radius
self.k1 = k1
self.k2 = k2
def pick_seeds(self, dists, scores, R, max_num):
"""
Select seeding points using Non Maximum Suppression. (here we only support bs=1)
Input:
- dists: [bs, num_corr, num_corr] src keypoints distance matrix
- scores: [bs, num_corr] initial confidence of each correspondence
- R: float radius of nms
- max_num: int maximum number of returned seeds
Output:
- picked_seeds: [bs, num_seeds] the index to the seeding correspondences
"""
assert scores.shape[0] == 1
# parallel Non Maximum Suppression (more efficient)
score_relation = scores.T >= scores # [num_corr, num_corr], save the relation of leading_eig
# score_relation[dists[0] >= R] = 1 # mask out the non-neighborhood node
score_relation = score_relation.bool() | (dists[0] >= R).bool()
is_local_max = score_relation.min(-1)[0].float()
score_local_max = scores * is_local_max
sorted_score = torch.argsort(score_local_max, dim=1, descending=True)
# max_num = scores.shape[1]
return_idx = sorted_score[:, 0: max_num].detach()
return return_idx
def cal_seed_trans(self, seeds, SC2_measure, src_keypts, tgt_keypts):
"""
Calculate the transformation for each seeding correspondences.
Input:
- seeds: [bs, num_seeds] the index to the seeding correspondence
- SC2_measure: [bs, num_corr, num_channels]
- src_keypts: [bs, num_corr, 3]
- tgt_keypts: [bs, num_corr, 3]
Output: leading eigenvector
- final_trans: [bs, 4, 4] best transformation matrix (after post refinement) for each batch.
"""
bs, num_corr, num_channels = SC2_measure.shape[0], SC2_measure.shape[1], SC2_measure.shape[2]
k1 = self.k1
k2 = self.k2
if k1 > num_channels:
k1 = 4
k2 = 4
#################################
# The first stage consensus set sampling
# Finding the k1 nearest neighbors around each seed
#################################
sorted_score = torch.argsort(SC2_measure, dim=2, descending=True)
knn_idx = sorted_score[:, :, 0: k1]
sorted_value, _ = torch.sort(SC2_measure, dim=2, descending=True)
idx_tmp = knn_idx.contiguous().view([bs, -1])
idx_tmp = idx_tmp[:, :, None]
idx_tmp = idx_tmp.expand(-1, -1, 3)
#################################
# construct the local SC2 measure of each consensus subset obtained in the first stage.
#################################
src_knn = src_keypts.gather(dim=1, index=idx_tmp).view([bs, -1, k1, 3]) # [bs, num_seeds, k, 3]
tgt_knn = tgt_keypts.gather(dim=1, index=idx_tmp).view([bs, -1, k1, 3])
src_dist = ((src_knn[:, :, :, None, :] - src_knn[:, :, None, :, :]) ** 2).sum(-1) ** 0.5
tgt_dist = ((tgt_knn[:, :, :, None, :] - tgt_knn[:, :, None, :, :]) ** 2).sum(-1) ** 0.5
cross_dist = torch.abs(src_dist - tgt_dist)
local_hard_SC_measure = (cross_dist < self.d_thre).float()
local_SC2_measure = torch.matmul(local_hard_SC_measure[:, :, :1, :], local_hard_SC_measure)
#################################
# perform second stage consensus set sampling
#################################
sorted_score = torch.argsort(local_SC2_measure, dim=3, descending=True)
knn_idx_fine = sorted_score[:, :, :, 0: k2]
#################################
# construct the soft SC2 matrix of the consensus set
#################################
num = knn_idx_fine.shape[1]
knn_idx_fine = knn_idx_fine.contiguous().view([bs, num, -1])[:, :, :, None]
knn_idx_fine = knn_idx_fine.expand(-1, -1, -1, 3)
src_knn_fine = src_knn.gather(dim=2, index=knn_idx_fine).view([bs, -1, k2, 3]) # [bs, num_seeds, k, 3]
tgt_knn_fine = tgt_knn.gather(dim=2, index=knn_idx_fine).view([bs, -1, k2, 3])
src_dist = ((src_knn_fine[:, :, :, None, :] - src_knn_fine[:, :, None, :, :]) ** 2).sum(-1) ** 0.5
tgt_dist = ((tgt_knn_fine[:, :, :, None, :] - tgt_knn_fine[:, :, None, :, :]) ** 2).sum(-1) ** 0.5
cross_dist = torch.abs(src_dist - tgt_dist)
local_hard_measure = (cross_dist < self.d_thre * 2).float()
local_SC2_measure = torch.matmul(local_hard_measure, local_hard_measure) / k2
local_SC_measure = torch.clamp(1 - cross_dist ** 2 / self.d_thre ** 2, min=0)
# local_SC2_measure = local_SC_measure * local_SC2_measure
local_SC2_measure = local_SC_measure
local_SC2_measure = local_SC2_measure.view([-1, k2, k2])
#################################
# Power iteratation to get the inlier probability
#################################
local_SC2_measure[:, torch.arange(local_SC2_measure.shape[1]), torch.arange(local_SC2_measure.shape[1])] = 0
total_weight = self.cal_leading_eigenvector(local_SC2_measure, method='power')
total_weight = total_weight.view([bs, -1, k2])
total_weight = total_weight / (torch.sum(total_weight, dim=-1, keepdim=True) + 1e-6)
#################################
# calculate the transformation by weighted least-squares for each subsets in parallel
#################################
total_weight = total_weight.view([-1, k2])
src_knn = src_knn_fine
tgt_knn = tgt_knn_fine
src_knn, tgt_knn = src_knn.view([-1, k2, 3]), tgt_knn.view([-1, k2, 3])
#################################
# compute the rigid transformation for each seed by the weighted SVD
#################################
seedwise_trans = rigid_transform_3d(src_knn, tgt_knn, total_weight)
seedwise_trans = seedwise_trans.view([bs, -1, 4, 4])
#################################
# calculate the inlier number for each hypothesis, and find the best transformation for each point cloud pair
#################################
pred_position = torch.einsum('bsnm,bmk->bsnk', seedwise_trans[:, :, :3, :3],
src_keypts.permute(0, 2, 1)) + seedwise_trans[:, :, :3,
3:4] # [bs, num_seeds, num_corr, 3]
#################################
# calculate the inlier number for each hypothesis, and find the best transformation for each point cloud pair
#################################
pred_position = pred_position.permute(0, 1, 3, 2)
L2_dis = torch.norm(pred_position - tgt_keypts[:, None, :, :], dim=-1) # [bs, num_seeds, num_corr]
seedwise_fitness = torch.sum((L2_dis < self.inlier_threshold).float(), dim=-1) # [bs, num_seeds]
batch_best_guess = seedwise_fitness.argmax(dim=1)
best_guess_ratio = seedwise_fitness[0, batch_best_guess]
final_trans = seedwise_trans.gather(dim=1,index=batch_best_guess[:, None, None, None].expand(-1, -1, 4, 4)).squeeze(1)
return final_trans
def cal_leading_eigenvector(self, M, method='power'):
"""
Calculate the leading eigenvector using power iteration algorithm or torch.symeig
Input:
- M: [bs, num_corr, num_corr] the compatibility matrix
- method: select different method for calculating the learding eigenvector.
Output:
- solution: [bs, num_corr] leading eigenvector
"""
if method == 'power':
# power iteration algorithm
leading_eig = torch.ones_like(M[:, :, 0:1])
leading_eig_last = leading_eig
for i in range(self.num_iterations):
leading_eig = torch.bmm(M, leading_eig)
leading_eig = leading_eig / (torch.norm(leading_eig, dim=1, keepdim=True) + 1e-6)
if torch.allclose(leading_eig, leading_eig_last):
break
leading_eig_last = leading_eig
leading_eig = leading_eig.squeeze(-1)
return leading_eig
elif method == 'eig': # cause NaN during back-prop
e, v = torch.symeig(M, eigenvectors=True)
leading_eig = v[:, :, -1]
return leading_eig
else:
exit(-1)
def cal_confidence(self, M, leading_eig, method='eig_value'):
"""
Calculate the confidence of the spectral matching solution based on spectral analysis.
Input:
- M: [bs, num_corr, num_corr] the compatibility matrix
- leading_eig [bs, num_corr] the leading eigenvector of matrix M
Output:
- confidence
"""
if method == 'eig_value':
# max eigenvalue as the confidence (Rayleigh quotient)
max_eig_value = (leading_eig[:, None, :] @ M @ leading_eig[:, :, None]) / (
leading_eig[:, None, :] @ leading_eig[:, :, None])
confidence = max_eig_value.squeeze(-1)
return confidence
elif method == 'eig_value_ratio':
# max eigenvalue / second max eigenvalue as the confidence
max_eig_value = (leading_eig[:, None, :] @ M @ leading_eig[:, :, None]) / (
leading_eig[:, None, :] @ leading_eig[:, :, None])
# compute the second largest eigen-value
B = M - max_eig_value * leading_eig[:, :, None] @ leading_eig[:, None, :]
solution = torch.ones_like(B[:, :, 0:1])
for i in range(self.num_iterations):
solution = torch.bmm(B, solution)
solution = solution / (torch.norm(solution, dim=1, keepdim=True) + 1e-6)
solution = solution.squeeze(-1)
second_eig = solution
second_eig_value = (second_eig[:, None, :] @ B @ second_eig[:, :, None]) / (
second_eig[:, None, :] @ second_eig[:, :, None])
confidence = max_eig_value / second_eig_value
return confidence
elif method == 'xMx':
# max xMx as the confidence (x is the binary solution)
# rank = torch.argsort(leading_eig, dim=1, descending=True)[:, 0:int(M.shape[1]*self.ratio)]
# binary_sol = torch.zeros_like(leading_eig)
# binary_sol[0, rank[0]] = 1
confidence = leading_eig[:, None, :] @ M @ leading_eig[:, :, None]
confidence = confidence.squeeze(-1) / M.shape[1]
return confidence
def post_refinement(self, initial_trans, src_keypts, tgt_keypts, it_num, weights=None):
"""
Perform post refinement using the initial transformation matrix, only adopted during testing.
Input
- initial_trans: [bs, 4, 4]
- src_keypts: [bs, num_corr, 3]
- tgt_keypts: [bs, num_corr, 3]
- weights: [bs, num_corr]
Output:
- final_trans: [bs, 4, 4]
"""
assert initial_trans.shape[0] == 1
inlier_threshold = 1.2
# inlier_threshold_list = [self.inlier_threshold] * it_num
if self.inlier_threshold == 0.10: # for 3DMatch
inlier_threshold_list = [0.10] * it_num
else: # for KITTI
inlier_threshold_list = [1.2] * it_num
previous_inlier_num = 0
for inlier_threshold in inlier_threshold_list:
warped_src_keypts = transform(src_keypts, initial_trans)
L2_dis = torch.norm(warped_src_keypts - tgt_keypts, dim=-1)
pred_inlier = (L2_dis < inlier_threshold)[0] # assume bs = 1
inlier_num = torch.sum(pred_inlier)
if abs(int(inlier_num - previous_inlier_num)) < 1:
break
else:
previous_inlier_num = inlier_num
initial_trans = rigid_transform_3d(
A=src_keypts[:, pred_inlier, :],
B=tgt_keypts[:, pred_inlier, :],
## https://link.springer.com/article/10.1007/s10589-014-9643-2
# weights=None,
weights=1 / (1 + (L2_dis / inlier_threshold) ** 2)[:, pred_inlier],
# weights=((1-L2_dis/inlier_threshold)**2)[:, pred_inlier]
)
return initial_trans
def match_pair(self, src_keypts, tgt_keypts, src_features, tgt_features):
N_src = src_features.shape[1]
N_tgt = tgt_features.shape[1]
# use all point or sample points.
if self.num_node == 'all':
src_sel_ind = np.arange(N_src)
tgt_sel_ind = np.arange(N_tgt)
else:
src_sel_ind = np.random.choice(N_src, self.num_node)
tgt_sel_ind = np.random.choice(N_tgt, self.num_node)
src_desc = src_features[:, src_sel_ind, :]
tgt_desc = tgt_features[:, tgt_sel_ind, :]
src_keypts = src_keypts[:, src_sel_ind, :]
tgt_keypts = tgt_keypts[:, tgt_sel_ind, :]
# match points in feature space.
distance = torch.sqrt(2 - 2 * (src_desc[0] @ tgt_desc[0].T) + 1e-6)
distance = distance.unsqueeze(0)
source_idx = torch.argmin(distance[0], dim=1)
corr = torch.cat([torch.arange(source_idx.shape[0])[:, None].cuda(), source_idx[:, None]], dim=-1)
# generate correspondences
src_keypts_corr = src_keypts[:, corr[:, 0]]
tgt_keypts_corr = tgt_keypts[:, corr[:, 1]]
return src_keypts_corr, tgt_keypts_corr
def SC2_PCR(self, src_keypts, tgt_keypts):
"""
Input:
- src_keypts: [bs, num_corr, 3]
- tgt_keypts: [bs, num_corr, 3]
Output:
- pred_trans: [bs, 4, 4], the predicted transformation matrix.
- pred_labels: [bs, num_corr], the predicted inlier/outlier label (0,1), for classification loss calculation.
"""
bs, num_corr = src_keypts.shape[0], tgt_keypts.shape[1]
#################################
# downsample points
#################################
if num_corr > self.max_points:
src_keypts = src_keypts[:, :self.max_points, :]
tgt_keypts = tgt_keypts[:, :self.max_points, :]
num_corr = self.max_points
#################################
# compute cross dist
#################################
src_dist = torch.norm((src_keypts[:, :, None, :] - src_keypts[:, None, :, :]), dim=-1)
target_dist = torch.norm((tgt_keypts[:, :, None, :] - tgt_keypts[:, None, :, :]), dim=-1)
cross_dist = torch.abs(src_dist - target_dist)
#################################
# compute first order measure
#################################
SC_dist_thre = self.d_thre
SC_measure = torch.clamp(1.0 - cross_dist ** 2 / SC_dist_thre ** 2, min=0)
hard_SC_measure = (cross_dist < SC_dist_thre).float()
#################################
# select reliable seed correspondences
#################################
confidence = self.cal_leading_eigenvector(SC_measure, method='power')
seeds = self.pick_seeds(src_dist, confidence, R=self.nms_radius, max_num=int(num_corr * self.ratio))
#################################
# compute second order measure
#################################
SC2_dist_thre = self.d_thre / 2
hard_SC_measure_tight = (cross_dist < SC2_dist_thre).float()
seed_hard_SC_measure = hard_SC_measure.gather(dim=1,
index=seeds[:, :, None].expand(-1, -1, num_corr))
seed_hard_SC_measure_tight = hard_SC_measure_tight.gather(dim=1,
index=seeds[:, :, None].expand(-1, -1, num_corr))
SC2_measure = torch.matmul(seed_hard_SC_measure_tight, hard_SC_measure_tight) * seed_hard_SC_measure
#################################
# compute the seed-wise transformations and select the best one
#################################
final_trans = self.cal_seed_trans(seeds, SC2_measure, src_keypts, tgt_keypts)
#################################
# refine the result by recomputing the transformation over the whole set
#################################
final_trans = self.post_refinement(final_trans, src_keypts, tgt_keypts, 20)
return final_trans
def estimator(self, src_keypts, tgt_keypts, src_features, tgt_features):
"""
Input:
- src_keypts: [bs, num_corr, 3]
- tgt_keypts: [bs, num_corr, 3]
- src_features: [bs, num_corr, C]
- tgt_features: [bs, num_corr, C]
Output:
- pred_trans: [bs, 4, 4], the predicted transformation matrix
- pred_trans: [bs, num_corr], the predicted inlier/outlier label (0,1)
- src_keypts_corr: [bs, num_corr, 3], the source points in the matched correspondences
- tgt_keypts_corr: [bs, num_corr, 3], the target points in the matched correspondences
"""
#################################
# generate coarse correspondences
#################################
src_keypts_corr, tgt_keypts_corr = self.match_pair(src_keypts, tgt_keypts, src_features, tgt_features)
#################################
# use the proposed SC2-PCR to estimate the rigid transformation
#################################
pred_trans = self.SC2_PCR(src_keypts_corr, tgt_keypts_corr)
frag1_warp = transform(src_keypts_corr, pred_trans)
distance = torch.sum((frag1_warp - tgt_keypts_corr) ** 2, dim=-1) ** 0.5
pred_labels = (distance < self.inlier_threshold).float()
return pred_trans, pred_labels, src_keypts_corr, tgt_keypts_corr
================================================
FILE: benchmark_utils.py
================================================
import torch
import numpy as np
import random
import math
import open3d as o3d
from utils.pointcloud import make_point_cloud
def exact_auc(errors, thresholds):
"""
Calculate the exact area under curve, borrow from https://github.com/magicleap/SuperGluePretrainedNetwork
"""
sort_idx = np.argsort(errors)
errors = np.array(errors.copy())[sort_idx]
recall = (np.arange(len(errors)) + 1) / len(errors)
errors = np.r_[0., errors]
recall = np.r_[0., recall]
aucs = []
for t in thresholds:
last_index = np.searchsorted(errors, t)
r = np.r_[recall[:last_index], recall[last_index - 1]]
e = np.r_[errors[:last_index], t]
aucs.append(np.trapz(r, x=e) / t)
return aucs
def set_seed(seed=51):
"""
Set the random seed for reproduce the results.
"""
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def icp_refine(src_keypts, tgt_keypts, pred_trans):
"""
ICP algorithm to refine the initial transformation
Input:
- src_keypts [1, num_corr, 3] FloatTensor
- tgt_keypts [1, num_corr, 3] FloatTensor
- pred_trans [1, 4, 4] FloatTensor, initial transformation
"""
src_pcd = make_point_cloud(src_keypts.detach().cpu().numpy()[0])
tgt_pcd = make_point_cloud(tgt_keypts.detach().cpu().numpy()[0])
initial_trans = pred_trans[0].detach().cpu().numpy()
# change the convension of transforamtion because open3d use left multi.
refined_T = o3d.registration.registration_icp(
src_pcd, tgt_pcd, 0.10, initial_trans,
o3d.registration.TransformationEstimationPointToPoint()).transformation
refined_T = torch.from_numpy(refined_T[None, :, :]).to(pred_trans.device).float()
return refined_T
def is_rotation_matrix(R):
"""
Checks if a matrix is a valid rotation matrix.
Input:
- R: [3, 3] rotation matrix
Output:
- True/False
"""
Rt = np.transpose(R)
shouldBeIdentity = np.dot(Rt, R)
I = np.identity(3, dtype=R.dtype)
n = np.linalg.norm(I - shouldBeIdentity)
return n < 1e-3
def rot_to_euler(R):
"""
Convert the rotation matrix to euler angles(degree)
Input:
- R: [3, 3] rotation matrix
Output:
- alpha. [3], the rotation angle (in degrees) along x,y,z axis.
"""
assert (is_rotation_matrix(R))
sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])
singular = sy < 1e-6
if not singular:
x = math.atan2(R[2, 1], R[2, 2])
y = math.atan2(-R[2, 0], sy)
z = math.atan2(R[1, 0], R[0, 0])
else:
x = math.atan2(-R[1, 2], R[1, 1])
y = math.atan2(-R[2, 0], sy)
z = 0
return np.array([x * 180 / np.pi, y * 180 / np.pi, z * 180 / np.pi])
================================================
FILE: benchmark_utils_predator.py
================================================
"""
Script for benchmarking the 3DMatch test dataset.
Author: Zan Gojcic, Shengyu Huang
Last modified: 30.11.2020
"""
import numpy as np
import os,sys,glob,torch,math
from collections import defaultdict
import nibabel.quaternions as nq
def rotation_error(R1, R2):
"""
Torch batch implementation of the rotation error between the estimated and the ground truth rotatiom matrix.
Rotation error is defined as r_e = \arccos(\frac{Trace(\mathbf{R}_{ij}^{T}\mathbf{R}_{ij}^{\mathrm{GT}) - 1}{2})
Args:
R1 (torch tensor): Estimated rotation matrices [b,3,3]
R2 (torch tensor): Ground truth rotation matrices [b,3,3]
Returns:
ae (torch tensor): Rotation error in angular degreees [b,1]
"""
R_ = torch.matmul(R1.transpose(1,2), R2)
e = torch.stack([(torch.trace(R_[_, :, :]) - 1) / 2 for _ in range(R_.shape[0])], dim=0).unsqueeze(1)
# Clamp the errors to the valid range (otherwise torch.acos() is nan)
e = torch.clamp(e, -1, 1, out=None)
ae = torch.acos(e)
pi = torch.Tensor([math.pi])
ae = 180. * ae / pi.to(ae.device).type(ae.dtype)
return ae
def translation_error(t1, t2):
"""
Torch batch implementation of the rotation error between the estimated and the ground truth rotatiom matrix.
Rotation error is defined as r_e = \arccos(\frac{Trace(\mathbf{R}_{ij}^{T}\mathbf{R}_{ij}^{\mathrm{GT}) - 1}{2})
Args:
t1 (torch tensor): Estimated translation vectors [b,3,1]
t2 (torch tensor): Ground truth translation vectors [b,3,1]
Returns:
te (torch tensor): translation error in meters [b,1]
"""
return torch.norm(t1-t2, dim=(1, 2))
def computeTransformationErr(trans, info):
"""
Computer the transformation error as an approximation of the RMSE of corresponding points.
More informaiton at http://redwood-data.org/indoor/registration.html
Args:
trans (numpy array): transformation matrices [n,4,4]
info (numpy array): covariance matrices of the gt transformation paramaters [n,4,4]
Returns:
p (float): transformation error
"""
t = trans[:3, 3]
r = trans[:3, :3]
q = nq.mat2quat(r)
er = np.concatenate([t, q[1:]], axis=0)
p = er.reshape(1, 6) @ info @ er.reshape(6, 1) / info[0, 0]
return p.item()
def read_trajectory(filename, dim=4):
"""
Function that reads a trajectory saved in the 3DMatch/Redwood format to a numpy array.
Format specification can be found at http://redwood-data.org/indoor/fileformat.html
Args:
filename (str): path to the '.txt' file containing the trajectory data
dim (int): dimension of the transformation matrix (4x4 for 3D data)
Returns:
final_keys (dict): indices of pairs with more than 30% overlap (only this ones are included in the gt file)
traj (numpy array): gt pairwise transformation matrices for n pairs[n,dim, dim]
"""
with open(filename) as f:
lines = f.readlines()
# Extract the point cloud pairs
keys = lines[0::(dim+1)]
temp_keys = []
for i in range(len(keys)):
temp_keys.append(keys[i].split('\t')[0:3])
final_keys = []
for i in range(len(temp_keys)):
final_keys.append([temp_keys[i][0].strip(), temp_keys[i][1].strip(), temp_keys[i][2].strip()])
traj = []
for i in range(len(lines)):
if i % 5 != 0:
traj.append(lines[i].split('\t')[0:dim])
traj = np.asarray(traj, dtype=np.float).reshape(-1,dim,dim)
final_keys = np.asarray(final_keys)
return final_keys, traj
def read_trajectory_info(filename, dim=6):
"""
Function that reads the trajectory information saved in the 3DMatch/Redwood format to a numpy array.
Information file contains the variance-covariance matrix of the transformation paramaters.
Format specification can be found at http://redwood-data.org/indoor/fileformat.html
Args:
filename (str): path to the '.txt' file containing the trajectory information data
dim (int): dimension of the transformation matrix (4x4 for 3D data)
Returns:
n_frame (int): number of fragments in the scene
cov_matrix (numpy array): covariance matrix of the transformation matrices for n pairs[n,dim, dim]
"""
with open(filename) as fid:
contents = fid.readlines()
n_pairs = len(contents) // 7
assert (len(contents) == 7 * n_pairs)
info_list = []
n_frame = 0
for i in range(n_pairs):
frame_idx0, frame_idx1, n_frame = [int(item) for item in contents[i * 7].strip().split()]
info_matrix = np.concatenate(
[np.fromstring(item, sep='\t').reshape(1, -1) for item in contents[i * 7 + 1:i * 7 + 7]], axis=0)
info_list.append(info_matrix)
cov_matrix = np.asarray(info_list, dtype=np.float).reshape(-1,dim,dim)
return n_frame, cov_matrix
def extract_corresponding_trajectors(est_pairs,gt_pairs, gt_traj):
"""
Extract only those transformation matrices from the ground truth trajectory that are also in the estimated trajectory.
Args:
est_pairs (numpy array): indices of point cloud pairs with enough estimated overlap [m, 3]
gt_pairs (numpy array): indices of gt overlaping point cloud pairs [n,3]
gt_traj (numpy array): 3d array of the gt transformation parameters [n,4,4]
Returns:
ext_traj (numpy array): gt transformation parameters for the point cloud pairs from est_pairs [m,4,4]
"""
ext_traj = np.zeros((len(est_pairs), 4, 4))
for est_idx, pair in enumerate(est_pairs):
pair[2] = gt_pairs[0][2]
gt_idx = np.where((gt_pairs == pair).all(axis=1))[0]
ext_traj[est_idx,:,:] = gt_traj[gt_idx,:,:]
return ext_traj
def evaluate_registration(num_fragment, result, result_pairs, gt_pairs, gt, gt_info, err2=0.2):
"""
Evaluates the performance of the registration algorithm according to the evaluation protocol defined
by the 3DMatch/Redwood datasets. The evaluation protocol can be found at http://redwood-data.org/indoor/registration.html
Args:
num_fragment (int): path to the '.txt' file containing the trajectory information data
result (numpy array): estimated transformation matrices [n,4,4]
result_pairs (numpy array): indices of the point cloud for which the transformation matrix was estimated (m,3)
gt_pairs (numpy array): indices of the ground truth overlapping point cloud pairs (n,3)
gt (numpy array): ground truth transformation matrices [n,4,4]
gt_cov (numpy array): covariance matrix of the ground truth transfromation parameters [n,6,6]
err2 (float): threshold for the RMSE of the gt correspondences (default: 0.2m)
Returns:
precision (float): mean registration precision over the scene (not so important because it can be increased see papers)
recall (float): mean registration recall over the scene (deciding parameter for the performance of the algorithm)
"""
err2 = err2 ** 2
gt_mask = np.zeros((num_fragment, num_fragment), dtype=np.int)
flags=[]
for idx in range(gt_pairs.shape[0]):
i = int(gt_pairs[idx,0])
j = int(gt_pairs[idx,1])
# Only non consecutive pairs are tested
if j - i > 1:
gt_mask[i, j] = idx
n_gt = np.sum(gt_mask > 0)
good = 0
n_res = 0
for idx in range(result_pairs.shape[0]):
i = int(result_pairs[idx,0])
j = int(result_pairs[idx,1])
pose = result[idx,:,:]
if gt_mask[i, j] > 0:
n_res += 1
gt_idx = gt_mask[i, j]
p = computeTransformationErr(np.linalg.inv(gt[gt_idx,:,:]) @ pose, gt_info[gt_idx,:,:])
if p <= err2:
good += 1
flags.append(0)
else:
flags.append(1)
else:
flags.append(2)
if n_res == 0:
n_res += 1e6
precision = good * 1.0 / n_res
recall = good * 1.0 / n_gt
return precision, recall, flags
================================================
FILE: benchmarks/3DLoMatch/7-scenes-redkitchen/gt.info
================================================
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gitextract_aendpbmi/
├── .gitignore
├── 3DLoMatch.pkl
├── LICENSE
├── README.md
├── SC2_PCR.py
├── benchmark_utils.py
├── benchmark_utils_predator.py
├── benchmarks/
│ └── 3DLoMatch/
│ ├── 7-scenes-redkitchen/
│ │ └── gt.info
│ ├── sun3d-home_at-home_at_scan1_2013_jan_1/
│ │ └── gt.info
│ ├── sun3d-home_md-home_md_scan9_2012_sep_30/
│ │ └── gt.info
│ ├── sun3d-hotel_uc-scan3/
│ │ └── gt.info
│ ├── sun3d-hotel_umd-maryland_hotel1/
│ │ └── gt.info
│ ├── sun3d-hotel_umd-maryland_hotel3/
│ │ └── gt.info
│ ├── sun3d-mit_76_studyroom-76-1studyroom2/
│ │ └── gt.info
│ └── sun3d-mit_lab_hj-lab_hj_tea_nov_2_2012_scan1_erika/
│ └── gt.info
├── common.py
├── config.py
├── config_json/
│ ├── config_3DLoMatch.json
│ ├── config_3DMatch.json
│ └── config_KITTI.json
├── dataset.py
├── environment.yml
├── evaluate_metric.py
├── test_3DLoMatch.py
├── test_3DMatch.py
├── test_KITTI.py
└── utils/
├── SE3.py
├── max_clique.py
├── pointcloud.py
├── sinkhorn.py
└── timer.py
SYMBOL INDEX (84 symbols across 15 files)
FILE: SC2_PCR.py
class Matcher (line 8) | class Matcher():
method __init__ (line 9) | def __init__(self,
method pick_seeds (line 33) | def pick_seeds(self, dists, scores, R, max_num):
method cal_seed_trans (line 61) | def cal_seed_trans(self, seeds, SC2_measure, src_keypts, tgt_keypts):
method cal_leading_eigenvector (line 168) | def cal_leading_eigenvector(self, M, method='power'):
method cal_confidence (line 196) | def cal_confidence(self, M, leading_eig, method='eig_value'):
method post_refinement (line 236) | def post_refinement(self, initial_trans, src_keypts, tgt_keypts, it_nu...
method match_pair (line 278) | def match_pair(self, src_keypts, tgt_keypts, src_features, tgt_features):
method SC2_PCR (line 305) | def SC2_PCR(self, src_keypts, tgt_keypts):
method estimator (line 367) | def estimator(self, src_keypts, tgt_keypts, src_features, tgt_features):
FILE: benchmark_utils.py
function exact_auc (line 9) | def exact_auc(errors, thresholds):
function set_seed (line 27) | def set_seed(seed=51):
function icp_refine (line 40) | def icp_refine(src_keypts, tgt_keypts, pred_trans):
function is_rotation_matrix (line 59) | def is_rotation_matrix(R):
function rot_to_euler (line 74) | def rot_to_euler(R):
FILE: benchmark_utils_predator.py
function rotation_error (line 14) | def rotation_error(R1, R2):
function translation_error (line 40) | def translation_error(t1, t2):
function computeTransformationErr (line 56) | def computeTransformationErr(trans, info):
function read_trajectory (line 78) | def read_trajectory(filename, dim=4):
function read_trajectory_info (line 118) | def read_trajectory_info(filename, dim=6):
function extract_corresponding_trajectors (line 151) | def extract_corresponding_trajectors(est_pairs,gt_pairs, gt_traj):
function evaluate_registration (line 174) | def evaluate_registration(num_fragment, result, result_pairs, gt_pairs, ...
FILE: common.py
function rigid_transform_3d (line 7) | def rigid_transform_3d(A, B, weights=None, weight_threshold=0):
function knn (line 48) | def knn(x, k, ignore_self=False, normalized=True):
class EdgeConv (line 72) | class EdgeConv(nn.Module):
method __init__ (line 73) | def __init__(self, in_dim, out_dim, k, idx=None):
method forward (line 81) | def forward(self, x):
class ContextNormalization (line 106) | class ContextNormalization(nn.Module):
method __init__ (line 107) | def __init__(self):
method forward (line 110) | def forward(self, x):
class PointCN (line 118) | class PointCN(nn.Module):
method __init__ (line 119) | def __init__(self, in_dim=6, num_layers=6, num_channels=128, act_pos='...
method forward (line 137) | def forward(self, x):
FILE: config.py
function add_argument_group (line 9) | def add_argument_group(name):
function str2bool (line 15) | def str2bool(v):
function get_config (line 94) | def get_config():
FILE: dataset.py
class ThreeDLoader (line 8) | class ThreeDLoader(data.Dataset):
method __init__ (line 9) | def __init__(self,
method get_data (line 52) | def get_data(self, index):
method __len__ (line 86) | def __len__(self):
method __loadlog__ (line 89) | def __loadlog__(self, gtpath):
class ThreeDLoMatchLoader (line 105) | class ThreeDLoMatchLoader(data.Dataset):
method __init__ (line 106) | def __init__(self,
method get_data (line 125) | def get_data(self, index):
method __len__ (line 190) | def __len__(self):
class KITTILoader (line 193) | class KITTILoader(data.Dataset):
method __init__ (line 194) | def __init__(self,
method get_data (line 218) | def get_data(self, index):
method __len__ (line 239) | def __len__(self):
FILE: evaluate_metric.py
class TransformationLoss (line 12) | class TransformationLoss(nn.Module):
method __init__ (line 13) | def __init__(self, re_thre=15, te_thre=30):
method forward (line 19) | def forward(self, trans, gt_trans, src_keypts, tgt_keypts, probs):
class ClassificationLoss (line 66) | class ClassificationLoss(nn.Module):
method __init__ (line 67) | def __init__(self, balanced=True):
method forward (line 71) | def forward(self, pred, gt, weight=None):
FILE: test_3DLoMatch.py
function eval_3DLoMatch_scene (line 18) | def eval_3DLoMatch_scene(loader, matcher, trans_evaluator, cls_evaluator...
function eval_3DLoMatch (line 81) | def eval_3DLoMatch(config):
function benchmark_predator (line 122) | def benchmark_predator(pred_poses, gt_folder):
FILE: test_3DMatch.py
function eval_3DMatch_scene (line 18) | def eval_3DMatch_scene(loader, matcher, trans_evaluator, cls_evaluator, ...
function eval_3DMatch (line 79) | def eval_3DMatch(config, use_icp):
FILE: test_KITTI.py
function eval_KITTI_per_pair (line 18) | def eval_KITTI_per_pair(loader, matcher, trans_evaluator, cls_evaluator,...
function eval_KITTI (line 87) | def eval_KITTI(config):
FILE: utils/SE3.py
function rotation_matrix (line 5) | def rotation_matrix(num_axis, augment_rotation):
function translation_matrix (line 32) | def translation_matrix(augment_translation):
function transform (line 43) | def transform(pts, trans):
function decompose_trans (line 59) | def decompose_trans(trans):
function integrate_trans (line 73) | def integrate_trans(R, t):
function concatenate (line 98) | def concatenate(trans1, trans2):
FILE: utils/max_clique.py
function pmc (line 14) | def pmc(ei,ej,nnodes,nnedges): #ei, ej is edge list whose index starts f...
FILE: utils/pointcloud.py
function make_point_cloud (line 5) | def make_point_cloud(pts):
function make_feature (line 12) | def make_feature(data, dim, npts):
function estimate_normal (line 20) | def estimate_normal(pcd, radius=0.06, max_nn=30):
FILE: utils/sinkhorn.py
function log_sinkhorn_iterations (line 9) | def log_sinkhorn_iterations(Z, log_mu, log_nu, iters: int):
function log_optimal_transport (line 26) | def log_optimal_transport(scores, bins0=None, bins1=None, alpha=None, it...
function rpmnet_sinkhorn (line 59) | def rpmnet_sinkhorn(log_score, bins0, bins1, iters: int):
FILE: utils/timer.py
class AverageMeter (line 4) | class AverageMeter(object):
method __init__ (line 7) | def __init__(self):
method reset (line 10) | def reset(self):
method update (line 17) | def update(self, val, n=1):
class Timer (line 26) | class Timer(object):
method __init__ (line 29) | def __init__(self):
method reset (line 36) | def reset(self):
method tic (line 43) | def tic(self):
method toc (line 48) | def toc(self, average=True):
Condensed preview — 31 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,299K chars).
[
{
"path": ".gitignore",
"chars": 1799,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 1064,
"preview": "MIT License\n\nCopyright (c) 2021 ZhiChen\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof"
},
{
"path": "README.md",
"chars": 6413,
"preview": "# SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration (CVPR 2022)\n\nPyTorch "
},
{
"path": "SC2_PCR.py",
"chars": 18888,
"preview": "import torch\nfrom common import knn, rigid_transform_3d\nfrom utils.SE3 import transform\nimport numpy as np\n\n\n\nclass Matc"
},
{
"path": "benchmark_utils.py",
"chars": 3006,
"preview": "import torch\nimport numpy as np\nimport random\nimport math\nimport open3d as o3d\nfrom utils.pointcloud import make_point_c"
},
{
"path": "benchmark_utils_predator.py",
"chars": 8071,
"preview": "\"\"\"\nScript for benchmarking the 3DMatch test dataset.\n\nAuthor: Zan Gojcic, Shengyu Huang\nLast modified: 30.11.2020\n\"\"\"\n\n"
},
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},
{
"path": "common.py",
"chars": 5082,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom utils.SE3 import *\n\n\ndef rigid_transform_3d(A, B"
},
{
"path": "config.py",
"chars": 4691,
"preview": "import argparse\nimport time\nimport os\n\narg_lists = []\nparser = argparse.ArgumentParser()\n\n\ndef add_argument_group(name):"
},
{
"path": "config_json/config_3DLoMatch.json",
"chars": 417,
"preview": "{\n \"CUDA_Devices\": \"2\",\n \"num_iterations\": 10,\n \"dataset\": \"3DLoMatch\",\n \"ratio\": 0.2,\n \"k1\": 30,\n \"k2"
},
{
"path": "config_json/config_3DMatch.json",
"chars": 410,
"preview": "{\n \"CUDA_Devices\": \"2\",\n \"num_iterations\": 10,\n \"dataset\": \"3DMatch\",\n \"ratio\": 0.2,\n \"k1\": 30,\n \"k2\":"
},
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"path": "config_json/config_KITTI.json",
"chars": 393,
"preview": "{\n \"CUDA_Devices\": \"1\",\n \"num_iterations\": 20,\n \"dataset\": \"KITTI\",\n \"ratio\": 0.2,\n \"k1\": 30,\n \"k2\": 2"
},
{
"path": "dataset.py",
"chars": 10768,
"preview": "import os\nimport pickle\nimport torch.utils.data as data\nfrom utils.SE3 import *\nimport torch\n\n\nclass ThreeDLoader(data."
},
{
"path": "environment.yml",
"chars": 3910,
"preview": "name: SC2_PCR\nchannels:\n - pytorch\n - open3d-admin\n - conda-forge\n - defaults\ndependencies:\n - _libgcc_mutex=0.1=ma"
},
{
"path": "evaluate_metric.py",
"chars": 4729,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom sklearn.metrics import recall"
},
{
"path": "test_3DLoMatch.py",
"chars": 10098,
"preview": "import json\nimport sys\nsys.path.append('.')\nimport argparse\nimport logging\nfrom tqdm import tqdm\nfrom easydict import Ea"
},
{
"path": "test_3DMatch.py",
"chars": 9028,
"preview": "import json\nimport sys\nsys.path.append('.')\nimport argparse\nimport logging\nimport torch\nimport numpy as np\nfrom tqdm imp"
},
{
"path": "test_KITTI.py",
"chars": 7463,
"preview": "import json\nimport sys\nsys.path.append('.')\nimport argparse\nimport logging\nfrom tqdm import tqdm\nfrom easydict import Ea"
},
{
"path": "utils/SE3.py",
"chars": 3818,
"preview": "import torch\nimport random\nimport numpy as np\n\ndef rotation_matrix(num_axis, augment_rotation):\n \"\"\"\n Sample rotat"
},
{
"path": "utils/max_clique.py",
"chars": 1443,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n'''\nCode borrowed from https://github.com/ryanrossi/pmc/blob/master/pmc.py\nFin"
},
{
"path": "utils/pointcloud.py",
"chars": 655,
"preview": "import open3d as o3d\nimport torch\n\n\ndef make_point_cloud(pts):\n if isinstance(pts, torch.Tensor):\n pts = pts.d"
},
{
"path": "utils/sinkhorn.py",
"chars": 2477,
"preview": "import torch\n\n'''\nReference:\nhttps://github.com/magicleap/SuperGluePretrainedNetwork/blob/c0626d58c843ee0464b0fa1dd4de40"
},
{
"path": "utils/timer.py",
"chars": 1332,
"preview": "import time\n\n\nclass AverageMeter(object):\n \"\"\"Computes and stores the average and current value\"\"\"\n\n def __init__("
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the ZhiChen902/SC2-PCR GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 31 files (1.2 MB), approximately 539.8k tokens, and a symbol index with 84 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.