https://github.com/colin97/msn-point-cloud-completion
Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)
https://github.com/colin97/msn-point-cloud-completion
3d-reconstruction auction-algorithm cuda earth-mover-distance earth-movers-distance minimum-spanning-tree point-cloud point-cloud-completion point-cloud-processing shape-completion
Last synced: 16 days ago
JSON representation
Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)
- Host: GitHub
- URL: https://github.com/colin97/msn-point-cloud-completion
- Owner: Colin97
- License: apache-2.0
- Created: 2019-12-21T01:14:23.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-04-08T23:05:52.000Z (about 3 years ago)
- Last Synced: 2025-03-31T12:36:38.553Z (23 days ago)
- Topics: 3d-reconstruction, auction-algorithm, cuda, earth-mover-distance, earth-movers-distance, minimum-spanning-tree, point-cloud, point-cloud-completion, point-cloud-processing, shape-completion
- Language: Python
- Homepage:
- Size: 5.44 MB
- Stars: 425
- Watchers: 4
- Forks: 57
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## MSN: Morphing and Sampling Network for Dense Point Cloud Completion
[[paper]](http://cseweb.ucsd.edu/~mil070/projects/AAAI2020/paper.pdf) [[data]](https://drive.google.com/drive/folders/1X143kUwtRtoPFxNRvUk9LuPlsf1lLKI7?usp=sharing)
MSN is a learning-based shape completion method which can preserve the known structures and generate dense and evenly distributed point clouds. See our AAAI 2020 [paper](http://cseweb.ucsd.edu/~mil070/projects/AAAI2020/paper.pdf) for more details.
In this project, we also provide an implementation for the Earth Mover's Distance (EMD) of point clouds, which is based on the auction algorithm and only needs $O(n)$ memory.
*with 32,768 points after completion*
### Usage
#### 1) Envrionment & prerequisites
- Pytorch 1.2.0
- CUDA 10.0
- Python 3.7
- [Visdom](https://github.com/facebookresearch/visdom)
- [Open3D](http://www.open3d.org/docs/release/index.html#python-api-index)
#### 2) Compile
Compile our extension modules:
cd emd
python3 setup.py install
cd expansion_penalty
python3 setup.py install
cd MDS
python3 setup.py install
#### 3) Download data and trained models
Download the data and trained models from [here](https://drive.google.com/drive/folders/1X143kUwtRtoPFxNRvUk9LuPlsf1lLKI7?usp=sharing). We don't provide the partial point clouds of the training set due to the large size. If you want to train the model, you can generate them with the [code](https://github.com/wentaoyuan/pcn/tree/master/render) and [ShapeNetCore.v1](https://shapenet.org/). We generate 50 partial point clouds for each CAD model.
#### 4) Train or validate
Run `python3 val.py` to validate the model or `python3 train.py` to train the model from scratch.
### EMD
We provide an EMD implementation for point cloud comparison, which only needs $O(n)$ memory and thus enables dense point clouds (with 10,000 points or over) and large batch size. It is based on an approximated algorithm (auction algorithm) and cannot guarantee a (but near) bijection assignment. It employs a parameter $\epsilon$ to balance the error rate and the speed of convergence. Smaller $\epsilon$ achieves more accurate results, but needs a longer time for convergence. The time complexity is $O(n^2k)$, where $k$ is the number of iterations. We set a $\epsilon = 0.005, k = 50$ during training and a $\epsilon = 0.002, k = 10000$ during testing. Please refer to`emd/README.md` for more details.
### Citation
If you find our work useful for your research, please cite:
```
@article{liu2019morphing,
title={Morphing and Sampling Network for Dense Point Cloud Completion},
author={Liu, Minghua and Sheng, Lu and Yang, Sheng and Shao, Jing and Hu, Shi-Min},
journal={arXiv preprint arXiv:1912.00280},
year={2019}
}
```
### License
This project Code is released under the Apache License 2.0 (refer to the LICENSE file for details).