PointNet和PointNet++的Pytorch实现

# Pytorch Implementation of PointNet and PointNet++ This repo is implementation for [PointNet](http://openaccess.thecvf.com/content_cvpr_2017/papers/Qi_PointNet_Deep_Learning_CVPR_2017_paper.pdf) and [PointNet++](http://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space.pdf) in pytorch. ## Update **2021/03/27:** (1) Release pre-trained models for semantic segmentation, where PointNet++ can achieve **53.5\%** mIoU. (2) Release pre-trained models for classification and part segmentation in `log/`. **2021/03/20:** Update codes for classification, including: (1) Add codes for training **ModelNet10** dataset. Using setting of ``--num_category 10``. (2) Add codes for running on CPU only. Using setting of ``--use_cpu``. (3) Add codes for offline data preprocessing to accelerate training. Using setting of ``--process_data``. (4) Add codes for training with uniform sampling. Using setting of ``--use_uniform_sample``. **2019/11/26:** (1) Fixed some errors in previous codes and added data augmentation tricks. Now classification by only 1024 points can achieve **92.8\%**! (2) Added testing codes, including classification and segmentation, and semantic segmentation with visualization. (3) Organized all models into `./models` files for easy using. ## Install The latest codes are tested on Ubuntu 16.04, CUDA10.1, PyTorch 1.6 and Python 3.7: ```shell conda install pytorch==1.6.0 cudatoolkit=10.1 -c pytorch ``` ## Classification (ModelNet10/40) ### Data Preparation Download alignment **ModelNet** [here](https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip) and save in `data/modelnet40_normal_resampled/`. ### Run You can run different modes with following codes. * If you want to use offline processing of data, you can use `--process_data` in the first run. You can download pre-processd data [here](https://drive.google.com/drive/folders/1_fBYbDO3XSdRt3DSbEBe41r5l9YpIGWF?usp=sharing) and save it in `data/modelnet40_normal_resampled/`. * If you want to train on ModelNet10, you can use `--num_category 10`. ```shell # ModelNet40 ## Select different models in ./models ## e.g., pointnet2_ssg without normal features 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 with normal features 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 with uniform sampling 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 ## Similar setting like ModelNet40, just using --num_category 10 ## e.g., pointnet2_ssg without normal features 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 ``` ### Performance | Model | Accuracy | |--|--| | PointNet (Official) | 89.2| | PointNet2 (Official) | 91.9 | | PointNet (Pytorch without normal) | 90.6| | PointNet (Pytorch with normal) | 91.4| | PointNet2_SSG (Pytorch without normal) | 92.2| | PointNet2_SSG (Pytorch with normal) | 92.4| | PointNet2_MSG (Pytorch with normal) | **92.8**| ## Part Segmentation (ShapeNet) ### Data Preparation Download alignment **ShapeNet** [here](https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip) and save in `data/shapenetcore_partanno_segmentation_benchmark_v0_normal/`. ### Run ``` ## 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 ``` ### Performance | Model | Inctance avg IoU| Class avg IoU |--|--|--| |PointNet (Official) |83.7|80.4 |PointNet2 (Official)|85.1 |81.9 |PointNet (Pytorch)| 84.3 |81.1| |PointNet2_SSG (Pytorch)| 84.9| 81.8 |PointNet2_MSG (Pytorch)| **85.4**| **82.5** ## Semantic Segmentation (S3DIS) ### Data Preparation Download 3D indoor parsing dataset (**S3DIS**) [here](http://buildingparser.stanford.edu/dataset.html) and save in `data/s3dis/Stanford3dDataset_v1.2_Aligned_Version/`. ``` cd data_utils python collect_indoor3d_data.py ``` Processed data will save in `data/stanford_indoor3d/`. ### Run ``` ## 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 ``` Visualization results will save in `log/sem_seg/pointnet2_sem_seg/visual/` and you can visualize these .obj file by [MeshLab](http://www.meshlab.net/). ### Performance |Model | Overall Acc |Class avg IoU | Checkpoint |--|--|--|--| | PointNet (Pytorch) | 78.9 | 43.7| [40.7MB](log/sem_seg/pointnet_sem_seg) | | PointNet2_ssg (Pytorch) | **83.0** | **53.5**| [11.2MB](log/sem_seg/pointnet2_sem_seg) | ## Visualization ### Using show3d_balls.py ``` ## build C++ code for visualization cd visualizer bash build.sh ## run one example python show3d_balls.py ```  ### Using MeshLab  ## Reference By [halimacc/pointnet3](https://github.com/halimacc/pointnet3)<br> [fxia22/pointnet.pytorch](https://github.com/fxia22/pointnet.pytorch)<br> [charlesq34/PointNet](https://github.com/charlesq34/pointnet) <br> [charlesq34/PointNet++](https://github.com/charlesq34/pointnet2) ## 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](https://github.com/Young98CN/pointconv_pytorch) * [On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks, CVPR'20](https://github.com/skywalker6174/3d-isometry-robust) * [Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions, ECCV'20](https://github.com/matheusgadelha/PointCloudLearningACD) * [PCT: Point Cloud Transformer](https://github.com/MenghaoGuo/PCT) * [PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point Cloud](https://github.com/lly007/PointStructuringNet) * [Stratified Transformer for 3D Point Cloud Segmentation, CVPR'22](https://github.com/dvlab-research/stratified-transformer)