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https://github.com/raoyongming/PointGLR
[CVPR 2020] Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
https://github.com/raoyongming/PointGLR
3d-point-clouds computer-vision deep-learning metric-learning representation-learning unsupervised-learning
Last synced: 18 days ago
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[CVPR 2020] Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
- Host: GitHub
- URL: https://github.com/raoyongming/PointGLR
- Owner: raoyongming
- License: mit
- Created: 2020-03-28T11:55:00.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-08-10T06:37:12.000Z (over 4 years ago)
- Last Synced: 2024-08-01T03:45:48.073Z (4 months ago)
- Topics: 3d-point-clouds, computer-vision, deep-learning, metric-learning, representation-learning, unsupervised-learning
- Language: Python
- Homepage:
- Size: 40 KB
- Stars: 116
- Watchers: 9
- Forks: 17
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
PointGLR
===
This repository contains the PyTorch implementation for paper __Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds__ (CVPR 2020) \[[arXiv](https://arxiv.org/abs/2003.12971)\]
If you find our work useful in your research, please consider citing:
```
@inproceedings{rao2020global,
title={Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds},
author={Rao, Yongming and Lu, Jiwen and Zhou, Jie},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
```
## Usage
### Requirement
- Python 3
- Pytorch 0.4
- CMake > 2.8
**Note**: The code is not not compatible with Pytorch >= 1.0 due to the C++/CUDA extensions.
### Building C++/CUDA Extensions for PointNet++
```
mkdir build && cd build
cmake .. && make
```
### Dataset Preparation
#### ModelNet
- Download ModelNet point clouds (XYZ and normal):
```
mkdir dataset && cd dataset
wget https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip
unzip modelnet40_normal_resampled.zip
```
- Preprocess dataset:
```
CUDA_VISIBLE_DEVICES=0 python tools/prepare_modelnet.py
```
#### ScanNet
You can download our prepared ScanNet dataset for object classification from [Google Drive](https://drive.google.com/file/d/176bUICMaEaDxoK4greUxV-3oseJJJ1v0/view?usp=sharing) and move the uncompressed data folder to `dataset/scannet`. The data structure should be:
```
dataset/scannet
├── train_files.txt
├── test_files.txt
├── train_0.h5
...
├── test_0.h5
...
```
#### ScanObjectNN
The ScanObjectNN dataset can be found [here](https://github.com/hkust-vgd/scanobjectnn). You can download the `h5_files.zipped` and move the uncompressed data folder to `dataset/ScanObjectNN`. The data sturcture should be:
```
dataset/ScanObjectNN
├── main_split
| └── training_objectdataset.h5
| ...
├── main_split_nobg
| └── training_objectdataset.h5
| ...
...
```
### Training & Evaluation
To train an SSG PointNet++ model on ModelNet:
```
bash train.sh exp_name pointnet2 modelnet
```
To train an SSG RSCNN model on ModelNet:
```
bash train.sh exp_name pointnet2 modelnet
```
You can modify `multiplier` in `cfgs/config.yaml` to train larger models. As a reference, the unsupervisedly trained 1x SSG PointNet++ and 1x SSG RSCNN models should have around 92.2% accuracy on ModelNet40. By increasing channel width (4x~5x), our best PointNet++ and RSCNN models achieved around 93.0% accuracy. The results might vary by 0.2%~0.5% between identical runs due to different random seed.
To obtain the results on ScanNet:
```
bash train.sh exp_name pointnet2 scannet
```
To obtain the results on ScanObjectNN:
```
bash train.sh exp_name pointnet2 scanobjectnn
```
Note that for experiments on ScanNet and ScanObjectNN, the feature extraction network is still trained on ModelNet. We only train the Linear SVM classifier on the corresponding dataset to obtain the classification results.
## Acknowledgement
The code is based on [Relation-Shape CNN](https://github.com/Yochengliu/Relation-Shape-CNN) and [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch).
## Contact
If you have any questions about our work, please contact