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https://github.com/prs-eth/OverlapPredator
[CVPR 2021, Oral] PREDATOR: Registration of 3D Point Clouds with Low Overlap.
https://github.com/prs-eth/OverlapPredator
3dvision attention-mechanism point-cloud registration transformer
Last synced: 3 months ago
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[CVPR 2021, Oral] PREDATOR: Registration of 3D Point Clouds with Low Overlap.
- Host: GitHub
- URL: https://github.com/prs-eth/OverlapPredator
- Owner: prs-eth
- License: mit
- Created: 2020-11-24T15:34:16.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-06-03T12:05:48.000Z (8 months ago)
- Last Synced: 2024-08-01T03:33:39.997Z (6 months ago)
- Topics: 3dvision, attention-mechanism, point-cloud, registration, transformer
- Language: Python
- Homepage: https://shengyuh.github.io/predator/index.html
- Size: 5.48 MB
- Stars: 481
- Watchers: 14
- Forks: 70
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## PREDATOR: Registration of 3D Point Clouds with Low Overlap (CVPR 2021, Oral)
This repository represents the official implementation of the paper:
### [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://arxiv.org/abs/2011.13005)
\*[Shengyu Huang](https://shengyuh.github.io), \*[Zan Gojcic](https://zgojcic.github.io/), [Mikhail Usvyatsov](https://aelphy.github.io), [Andreas Wieser](https://gseg.igp.ethz.ch/people/group-head/prof-dr--andreas-wieser.html), [Konrad Schindler](https://prs.igp.ethz.ch/group/people/person-detail.schindler.html)\
|[ETH Zurich](https://igp.ethz.ch/) | \* Equal contribution
For implementation using MinkowskiEngine backbone, please check [this](https://github.com/ShengyuH/OverlapPredator.Mink)
For more information, please see the [project website](https://overlappredator.github.io)
### Contact
If you have any questions, please let us know:
- Shengyu Huang {[email protected]}
- Zan Gojcic {[email protected]}
## News
- 2021-08-09: We've updated arxiv version of our [paper](https://arxiv.org/abs/2011.13005) with improved performance!
- 2021-06-02: Fix feature gathering bug in k-nn graph, please see improved performance in this [issue](https://github.com/overlappredator/OverlapPredator/issues/15). Stay tunned for updates on other experiments!
- 2021-05-31: Check our video and poster on [project page](https://overlappredator.github.io)!
- 2021-03-25: Camera ready is on arXiv! I also gave a talk on Predator(中文), you can find the recording here: [Bilibili](https://www.bilibili.com/video/BV1UK4y1U7Gs), [Youtube](https://www.youtube.com/watch?v=AZQGJa6R_4I&t=1563s)
- 2021-02-28: MinkowskiEngine-based PREDATOR [release](https://github.com/ShengyuH/OverlapPredator.Mink.git)
- 2020-11-30: Code and paper release
## Instructions
This code has been tested on
- Python 3.8.5, PyTorch 1.7.1, CUDA 11.2, gcc 9.3.0, GeForce RTX 3090/GeForce GTX 1080Ti
**Note**: We observe random data loader crashes due to memory issues, if you observe similar issues, please consider reducing the number of workers or increasing CPU RAM. We now released a sparse convolution-based Predator, have a look [here](https://github.com/ShengyuH/OverlapPredator.Mink.git)!
### Requirements
To create a virtual environment and install the required dependences please run:
```shell
git clone https://github.com/overlappredator/OverlapPredator.git
virtualenv predator; source predator/bin/activate
cd OverlapPredator; pip install -r requirements.txt
cd cpp_wrappers; sh compile_wrappers.sh; cd ..
```
in your working folder.
### Datasets and pretrained models
For KITTI dataset, please follow the instruction on [KITTI Odometry website](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) to download the KITTI odometry training set.
We provide
- preprocessed 3DMatch pairwise datasets (voxel-grid subsampled fragments together with their ground truth transformation matrices)
- raw dense 3DMatch datasets
- modelnet dataset
- pretrained models on 3DMatch, KITTI and Modelnet
The preprocessed data and models can be downloaded by running:
```shell
sh scripts/download_data_weight.sh
```
To download raw dense 3DMatch data, please run:
```shell
wget --no-check-certificate --show-progress https://share.phys.ethz.ch/~gsg/pairwise_reg/3dmatch.zip
unzip 3dmatch.zip
```
The folder is organised as follows:
- `3dmatch`
- `train`
- `7-scenes-chess`
- `fragments`
- `cloud_bin_*.ply`
- ...
- `poses`
- `cloud_bin_*.txt`
- ...
- ...
- `test`
### 3DMatch(Indoor)
#### Train
After creating the virtual environment and downloading the datasets, Predator can be trained using:
```shell
python main.py configs/train/indoor.yaml
```
#### Evaluate
For 3DMatch, to reproduce Table 2 in our main paper, we first extract features and overlap/matachability scores by running:
```shell
python main.py configs/test/indoor.yaml
```
the features together with scores will be saved to ```snapshot/indoor/3DMatch```. The estimation of the transformation parameters using RANSAC can then be carried out using:
```shell
for N_POINTS in 250 500 1000 2500 5000
do
python scripts/evaluate_predator.py --source_path snapshot/indoor/3DMatch --n_points $N_POINTS --benchmark 3DMatch --exp_dir snapshot/indoor/est_traj --sampling prob
done
```
dependent on ```n_points``` used by RANSAC, this might take a few minutes. The final results are stored in ```snapshot/indoor/est_traj/{benchmark}_{n_points}_prob/result```. To evaluate PREDATOR on 3DLoMatch benchmark, please also change ```3DMatch``` to ```3DLoMatch``` in ```configs/test/indoor.yaml```.
#### Demo
We prepared a small demo, which demonstrates the whole Predator pipeline using two random fragments from the 3DMatch dataset. To carry out the demo, please run:
```shell
python scripts/demo.py configs/test/indoor.yaml
```
The demo script will visualize input point clouds, inferred overlap regions, and point cloud aligned with the estimated transformation parameters:
### ModelNet(Synthetic)
#### Train
To train PREDATOR on ModelNet, please run:
```
python main.py configs/train/modelnet.yaml
```
We provide a small script to evaluate Predator on ModelNet test set, please run:
```
python main.py configs/test/modelnet.yaml
```
The rotation and translation errors could be better/worse than the reported ones due to randomness in RANSAC.
### KITTI(Outdoor)
We provide a small script to evaluate Predator on KITTI test set, after configuring KITTI dataset, please run:
```
python main.py configs/test/kitti.yaml
```
the results will be saved to the log file.
### Custom dataset
We have a few tips for train/test on custom dataset
- If it's similar indoor scenes, please run ```demo.py``` first to check the generalisation ability before retraining
- Remember to voxel-downsample the data in your data loader, see ```kitti.py``` for reference
### Citation
If you find this code useful for your work or use it in your project, please consider citing:
```shell
@InProceedings{Huang_2021_CVPR,
author = {Huang, Shengyu and Gojcic, Zan and Usvyatsov, Mikhail and Wieser, Andreas and Schindler, Konrad},
title = {Predator: Registration of 3D Point Clouds With Low Overlap},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {4267-4276}
}
```
### Acknowledgments
In this project we use (parts of) the official implementations of the followin works:
- [FCGF](https://github.com/chrischoy/FCGF) (KITTI preprocessing)
- [D3Feat](https://github.com/XuyangBai/D3Feat.pytorch) (KPConv backbone)
- [3DSmoothNet](https://github.com/zgojcic/3DSmoothNet) (3DMatch preparation)
- [MultiviewReg](https://github.com/zgojcic/3D_multiview_reg) (3DMatch benchmark)
- [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork) (Transformer part)
- [DGCNN](https://github.com/WangYueFt/dgcnn) (self-gnn)
- [RPMNet](https://github.com/yewzijian/RPMNet) (ModelNet preprocessing and evaluation)
We thank the respective authors for open sourcing their methods. We would also like to thank reviewers, especially reviewer 2 for his/her valuable inputs.