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PointCAT
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PointCAT
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# Pytorch Implementation of PointCAT This is the original PyTorch implementation of PointCAT: Cross-Attention Transformer for point cloud. Paper link: https://arxiv.org/pdf/2304.03012.pdf. ## Install The latest codes are tested on Windows 10, CUDA11.3, PyTorch 1.11.0 and Python 3.9: ```shell conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch ``` pointnet2_ops_lib: ```shell pip install pointnet2_ops_lib/. ``` ## Requirement ``` einops==0.6.0 h5py==3.6.0 matplotlib==3.5.2 numpy==1.21.5 open3d==0.15.1 pandas==1.4.2 pointnet2_ops==3.0.0 pyntcloud==0.3.1 scikit_learn==1.2.2 scipy==1.8.0 thop==0.1.1.post2209072238 timm==0.6.7 tqdm==4.64.0 ``` ## architecture for Shape Classification  ## Classification (ModelNet40) ## Data Preparation Download **ModelNet**40 dataset [here](https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip) and save in `classification_ModelNet40\utils\data`. ## Train & Test ```shell # Classification on ModelNet40 cd ./classification_ModelNet40 # Train python train.py --batch_size 16 --use_sgd --learning_rate 0.01 # Test python test.py --batch_size 16 --checkpoint your_chekpoint_root ``` ## Paper Result Download our best model in google drive [here](https://drive.google.com/drive/folders/1-hEw1u013bDSke3bgYShw-R6R9DgpnXq) and save in ./classification_ModenNet40/checkpoints/best_checkpoints. ```shell python test.py --checkpoint checkpoints/best_checkpoints ## Test result: 93.5% OA & 90.9% mAcc ``` ## Performance | Model | Inputs | Over All Accuracy (%) | Mean Accuracy (%) | Vote | |:-:|:-:|:-:|:-:|:-:| | PointCAT | 1024 points | 93.5% | 90.9% | × | ## Part Segmentation (ShapeNet) ## Data Preparation Download alignment **ShapeNetPart** [here](https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip) and save in `part_segmentation/data/shapenetcore_partanno_segmentation_benchmark_v0_normal/`. ## Run ```shell # Part Segmentation on ShapeNetPart cd ./part_segmentation # Train python train.py --exp_name "your_exp_name" --lr 0.003 --batch_size 16 --epochs 250 # Test python train.py --exp_name "your_exp_name" --eval ``` ## Performance | Cls. mIoU | Inst. mIoU | aero | bag | cap | car | chair | earphone | guitar | |:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:| |84.4 |86.0|83.0 |83.8 |90.1 |79.8 |90.2 |83.4 |91.8 | | **knife** | **lamp** | **laptop** | **motorbike** | **mug** | **pistol** | **rocket** | **skateboard** | **table** | | 87.8 | 82.5 | 95.9 | 76.1 | 95.4 | 84.9 | 68.5 | 83.1 | 84.3 | ## Visualization ```shell # Visualization of Part Segmentation Results cd ./part_segmentation img_root = './data/shapenetcore_partanno_segmentation_benchmark_v0_normal' model_root = './checkpoints/your_experiment_root' # dir to your checkpoints target_root = './results/your_result_root' # dir to save the result # Visualization python vis.py # ```   ## 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/`. The data structure be like: ``` ├─stanford_indoor3d | ├─Area_1_conferenceRoom_1.npy | ├─Area_1_conferenceRoom_2.npy | ├─Area_1_copyRoom_1.npy | ├─Area_1_hallway_1.npy | ├─Area_1_hallway_2.npy | ├─Area_1_hallway_3.npy ............... ``` ## Run ```shell # Semantic Segmentation on S3DIS cd ./seg_segmentation # Train python train.py --learning_rate 0.001 --optimizer AdamW --test_area 5 # Test python test_semseg.py --log_dir your_expriment_root --visiual --test_area 5 ``` ## Visualization Visualization results will save in `log/sem_seg/your_expriment_root/visual/` and you can transform these .obj files to .txt by these command: ```shell cd ./seg_segmentation/data # Vis python input_vis.py ```   ## Performance   ## Reference By [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} } @misc{guo2020pct, title={PCT: Point Cloud Transformer}, author={Meng-Hao Guo and Jun-Xiong Cai and Zheng-Ning Liu and Tai-Jiang Mu and Ralph R. Martin and Shi-Min Hu}, year={2020}, eprint={2012.09688}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{yang2023pointcat, title={PointCAT: Cross-Attention Transformer for point cloud}, author={Xincheng Yang and Mingze Jin and Weiji He and Qian Chen}, year={2023}, eprint={2304.03012}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Selected Projects using This Codebase [PCT: Point Cloud Transformer](https://github.com/MenghaoGuo/PCT) [Pytorch_Pointnet_Pointnet2](https://github.com/yanx27/Pointnet_Pointnet2_pytorch)
