https://github.com/Yochengliu/Relation-Shape-CNN

Relation-Shape Convolutional Neural Network for Point Cloud Analysis (CVPR 2019 Oral & Best paper finalist)
https://github.com/Yochengliu/Relation-Shape-CNN

3d-convolutional-network 3d-graphics 3d-point-clouds 3d-shape-recognition 3d-shape-segmentation artificial-intelligence convolutional-neural-network deep-learning geometric-deep-learning normal-estimation

Last synced: 3 months ago
JSON representation

Relation-Shape Convolutional Neural Network for Point Cloud Analysis (CVPR 2019 Oral & Best paper finalist)

• Host: GitHub
• URL: https://github.com/Yochengliu/Relation-Shape-CNN
• Owner: Yochengliu
• License: mit
• Created: 2019-04-04T09:34:58.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-09-30T11:36:21.000Z (almost 3 years ago)
• Last Synced: 2024-01-18T16:19:22.694Z (6 months ago)
• Topics: 3d-convolutional-network, 3d-graphics, 3d-point-clouds, 3d-shape-recognition, 3d-shape-segmentation, artificial-intelligence, convolutional-neural-network, deep-learning, geometric-deep-learning, normal-estimation
• Language: Python
• Homepage: https://yochengliu.github.io/Relation-Shape-CNN/
• Size: 18.7 MB
• Stars: 409
• Watchers: 19
• Forks: 74
• Open Issues: 20
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-stars - Yochengliu/Relation-Shape-CNN - Relation-Shape Convolutional Neural Network for Point Cloud Analysis (CVPR 2019 Oral & Best paper finalist) (Python)

README

        
Relation-Shape Convolutional Neural Network for Point Cloud Analysis

===

This repository contains the author's implementation in Pytorch for the paper:

__Relation-Shape Convolutional Neural Network for Point Cloud Analysis__ [[arXiv](https://arxiv.org/abs/1904.07601)] [[CVF](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Relation-Shape_Convolutional_Neural_Network_for_Point_Cloud_Analysis_CVPR_2019_paper.pdf)]




[Yongcheng Liu](https://yochengliu.github.io/), [Bin Fan](http://www.nlpr.ia.ac.cn/fanbin/), [Shiming Xiang](https://scholar.google.com/citations?user=0ggsACEAAAAJ&hl=zh-CN) and [Chunhong Pan](http://people.ucas.ac.cn/~0005314)




[__CVPR 2019 Oral & Best paper finalist__](http://cvpr2019.thecvf.com/)     __Project Page__: [https://yochengliu.github.io/Relation-Shape-CNN/](https://yochengliu.github.io/Relation-Shape-CNN/)

## Citation

If our paper is helpful for your research, please consider citing:   

```BibTex

        @inproceedings{liu2019rscnn,   

            author = {Yongcheng Liu and    

                            Bin Fan and    

                      Shiming Xiang and   

                           Chunhong Pan},   

            title = {Relation-Shape Convolutional Neural Network for Point Cloud Analysis},   

            booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},    

            pages = {8895--8904},  

            year = {2019}   

        }   

```

## Usage: Preparation

### Requirement

- Ubuntu 14.04

- Python 3 (recommend Anaconda3)

- Pytorch 0.3.\*/0.4.\*

- CMake > 2.8

- CUDA 8.0 + cuDNN 5.1

### Building Kernel

    git clone https://github.com/Yochengliu/Relation-Shape-CNN.git 

    cd Relation-Shape-CNN

- mkdir build && cd build

- cmake .. && make

### Dataset

__Shape Classification__

Download and unzip [ModelNet40](https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip) (415M). Replace `$data_root$` in `cfgs/config_*_cls.yaml` with the dataset parent path.

__ShapeNet Part Segmentation__

Download and unzip [ShapeNet Part](https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip) (674M). Replace `$data_root$` in `cfgs/config_*_partseg.yaml` with the dataset path.

## Usage: Training

### Shape Classification

    sh train_cls.sh

        

You can modify `relation_prior` in `cfgs/config_*_cls.yaml`. We have trained a Single-Scale-Neighborhood classification model in `cls` folder, whose accuracy is 92.38%.

        

### Shape Part Segmentation

    sh train_partseg.sh

        

We have trained a Multi-Scale-Neighborhood part segmentation model in `seg` folder, whose class mIoU and instance mIoU is 84.18% and 85.81% respectively.

## Usage: Evaluation

### Shape Classification

    Voting script: voting_evaluate_cls.py

        

You can use our model `cls/model_cls_ssn_iter_16218_acc_0.923825.pth` as the checkpoint in `config_ssn_cls.yaml`, and after this voting you will get an accuracy of 92.71% if all things go right.

### Shape Part Segmentation

    Voting script: voting_evaluate_partseg.py

        

You can use our model `seg/model_seg_msn_iter_57585_ins_0.858054_cls_0.841787.pth` as the checkpoint in `config_msn_partseg.yaml`.

## License

The code is released under MIT License (see LICENSE file for details).

## Acknowledgement

The code is heavily borrowed from [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch).

        

## Contact

If you have some ideas or questions about our research to share with us, please contact