Install

The latest codes are tested on Ubuntu 16.04, CUDA10.1, PyTorch 1.6 and Python 3.7:

conda install pytorch==1.6.0 cudatoolkit=10.1 -c pytorch

Introduction

• First, you should provide the data source "casex.def" and "fs_x.spef". Through these two types of files, you should change your files into "xxx.txt" format.
• Second, use "data/main.py" to produce dataset. The program will delete all files which has more than 256 columns.
• Third, use "cap_train.py" to train and test, all the results, including train loss and total MSE, will be stored in "result.txt"
• Fourth, the overfitting problem needs attention, you can change the total epoches in "cap_train.py".
• Last, you can examine the result in "result.txt", and the MSE of test can reach 1.653 in the best case.

Reference By

halimacc/pointnet3
fxia22/pointnet.pytorch
charlesq34/PointNet
charlesq34/PointNet++
yanx27/Pointnet_Pointnet2_pytorch

Citation

If you find this repo useful in your research, please consider citing it and our other works:

@article{Pytorch_Pointnet_Pointnet2,
      Author = {Xu Yan},
      Title = {Pointnet/Pointnet++ Pytorch},
      Journal = {https://github.com/yanx27/Pointnet_Pointnet2_pytorch},
      Year = {2019}
}

@InProceedings{yan2020pointasnl,
  title={PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling},
  author={Yan, Xu and Zheng, Chaoda and Li, Zhen and Wang, Sheng and Cui, Shuguang},
  journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}

@InProceedings{yan2021sparse,
  title={Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion},
  author={Yan, Xu and Gao, Jiantao and Li, Jie and Zhang, Ruimao, and Li, Zhen and Huang, Rui and Cui, Shuguang},
  journal={AAAI Conference on Artificial Intelligence ({AAAI})},
  year={2021}
}

@InProceedings{yan20222dpass,
      title={2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds}, 
      author={Xu Yan and Jiantao Gao and Chaoda Zheng and Chao Zheng and Ruimao Zhang and Shuguang Cui and Zhen Li},
      year={2022},
      journal={ECCV}
}

Selected Projects using This Codebase

• PointConv: Deep Convolutional Networks on 3D Point Clouds, CVPR'19
• On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks, CVPR'20
• Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions, ECCV'20
• PCT: Point Cloud Transformer
• PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point Cloud
• Stratified Transformer for 3D Point Cloud Segmentation, CVPR'22