PointNet 与 PointNet++ 的 PyTorch 实现

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

# ModelNet40
## Select different models in ./models 

## e.g., pointnet2_ssg 没有法向特征
python train_classification.py --model pointnet2_cls_ssg --log_dir pointnet2_cls_ssg
python test_classification.py --log_dir pointnet2_cls_ssg

## e.g., pointnet2_ssg 增加法向特征
python train_classification.py --model pointnet2_cls_ssg --use_normals --log_dir pointnet2_cls_ssg_normal
python test_classification.py --use_normals --log_dir pointnet2_cls_ssg_normal

## e.g., pointnet2_ssg 基于均匀采样
python train_classification.py --model pointnet2_cls_ssg --use_uniform_sample --log_dir pointnet2_cls_ssg_fps
python test_classification.py --use_uniform_sample --log_dir pointnet2_cls_ssg_fps

# ModelNet10
## 与 ModelNet40 的配置相同，仅使用 --num_category 10

## e.g., pointnet2_ssg 没有法向特征
python train_classification.py --model pointnet2_cls_ssg --log_dir pointnet2_cls_ssg --num_category 10
python test_classification.py --log_dir pointnet2_cls_ssg --num_category 10

## Check model in ./models 
## e.g., pointnet2_msg
python train_partseg.py --model pointnet2_part_seg_msg --normal --log_dir pointnet2_part_seg_msg
python test_partseg.py --normal --log_dir pointnet2_part_seg_msg

cd data_utils
python collect_indoor3d_data.py

## Check model in ./models 
## e.g., pointnet2_ssg
python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg
python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual

## build C++ code for visualization
cd visualizer
bash build.sh 
## run one example 
python show3d_balls.py

@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}
}