yanx27 / Pointnet_Pointnet2_pytorch

Pytorch Implementation of PointNet and PointNet++ This repo is implementation for PointNet and PointNet++ 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 . **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 files for easy using. Install The latest codes are tested on Ubuntu 16.04, CUDA10.1, PyTorch 1.6 and Python 3.7: Classification (ModelNet10/40) Data Preparation Download alignment **ModelNet** here and save in . Run You can run different modes with following codes. • If you want to use offline processing of data, you can use in the first run. You can download pre-processd data here and save it in . • If you want to train on ModelNet10, you can use . 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 and save in . Run 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 and save in . Processed data will save in . Run Visualization results will save in and you can visualize these .obj file by MeshLab. Performance |Model | Overall Acc |Class avg IoU | Checkpoint |--|--|--|--| | PointNet (Pytorch) | 78.9 | 43.7| 40.7MB | | PointNet2_ssg (Pytorch) | **83.0** | **53.5**| 11.2MB | Visualization Using show3d_balls.py Using MeshLab Reference By halimacc/pointnet3 fxia22/pointnet.pytorch charlesq34/PointNet charlesq34/PointNet++ Citation If you find this repo useful in your research, please consider citing it and our other works: 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