https://github.com/Yochengliu/DensePoint

DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (ICCV 2019)
https://github.com/Yochengliu/DensePoint

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

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DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (ICCV 2019)

• Host: GitHub
• URL: https://github.com/Yochengliu/DensePoint
• Owner: Yochengliu
• License: mit
• Created: 2019-08-01T03:05:31.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-09-30T11:43:20.000Z (almost 3 years ago)
• Last Synced: 2024-01-18T15:53:04.430Z (6 months ago)
• Topics: 3d-convolutional-network, 3d-graphics, 3d-point-clouds, 3d-shape-recognition, 3d-shape-segmentation, artificial-intelligence, convolutional-neural-networks, deep-learning, geometric-deep-learning, normal-estimation
• Language: Python
• Homepage:
• Size: 1.88 MB
• Stars: 118
• Watchers: 10
• Forks: 24
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-stars - Yochengliu/DensePoint - DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (ICCV 2019) (Python)

README

        
DensePoint

===

This repository contains the code in Pytorch for the paper:

__DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing__ [[arXiv](https://arxiv.org/abs/1909.03669)] [[CVF](http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_DensePoint_Learning_Densely_Contextual_Representation_for_Efficient_Point_Cloud_Processing_ICCV_2019_paper.pdf)]




[Yongcheng Liu](https://yochengliu.github.io/), [Bin Fan](http://www.nlpr.ia.ac.cn/fanbin/), [Gaofeng Meng](http://www.escience.cn/people/menggaofeng/index.html;jsessionid=EE2E193290F516D1BA8E2E35A09A9A08-n1), [Jiwen Lu](http://ivg.au.tsinghua.edu.cn/Jiwen_Lu/), [Shiming Xiang](https://scholar.google.com/citations?user=0ggsACEAAAAJ&hl=zh-CN) and [Chunhong Pan](http://people.ucas.ac.cn/~0005314)




[__ICCV 2019__](http://iccv2019.thecvf.com/)

## Citation

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

        @inproceedings{liu2019densepoint,   

            author = {Yongcheng Liu and    

                            Bin Fan and  

                       Gaofeng Meng and

                           Jiwen Lu and

                      Shiming Xiang and   

                           Chunhong Pan},   

            title = {DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing},   

            booktitle = {IEEE International Conference on Computer Vision (ICCV)},    

            pages = {5239--5248},  

            year = {2019}   

        }   

## Usage: Preparation

- Requirement

  - Ubuntu 14.04

  - Python 3 (recommend Anaconda3)

  - Pytorch 0.3.\*

  - CMake > 2.8

  - CUDA 8.0 + cuDNN 5.1

- Building Kernel

      git clone https://github.com/Yochengliu/DensePoint.git 

      cd DensePoint

      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.

## Usage: Training

- Shape Classification

      sh train_cls.sh

        

We have trained a 6-layer classification model in `cls` folder, whose accuracy is 92.38%.

## Usage: Evaluation

- Shape Classification

      Voting script: voting_evaluate_cls.py

        

You can use our model `cls/model_cls_L6_iter_36567_acc_0.923825.pth` as the checkpoint in `config_cls.yaml`, and after this voting you will get an accuracy of 92.5% if all things go right.

## 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