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APES: Attention-based Point Cloud Edge Sampling
<p> <a href="https://arxiv.org/pdf/2302.14673.pdf"> <img src="https://img.shields.io/badge/PDF-arXiv-brightgreen" /></a> <a href="https://junweizheng93.github.io/publications/APES/APES.html"> <img src="https://img.shields.io/badge/Project-Homepage-red" /></a> <a href="https://pytorch.org/"> <img src="https://img.shields.io/badge/Framework-PyTorch-orange" /></a> <a href="https://mmengine.readthedocs.io/en/latest/"> <img src="https://img.shields.io/badge/Framework-MMEngine-ff69b4" /></a> <a href="https://github.com/JunweiZheng93/APES/blob/main/LICENSE"> <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" /></a> </p>
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Homepage
This project is selected as a Highlight at CVPR 2023! For more information about the project, please refer to our project homepage.
Prerequisites
Install all necessary packages using:
conda create -n APES python=3.9 -y
conda activate APES
conda install pytorch==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia -y
pip install -r requirements.txt
Data
Download and preprocess the data using:
python utils/download_modelnet.py # for classification
python utils/download_shapenet.py # for segmentation
Train
Train models from scratch using:
# using single GPU
# command: bash utils/single_gpu_train.sh cfg_file
bash utils/single_gpu_train.sh configs/apes/apes_cls_local-modelnet-200epochs.py # for classification using local-based downsampling
bash utils/single_gpu_train.sh configs/apes/apes_cls_global-modelnet-200epochs.py # for classification using global-based downsampling
bash utils/single_gpu_train.sh configs/apes/apes_seg_local-shapenet-200epochs.py # for segmentation using local-based downsampling
bash utils/single_gpu_train.sh configs/apes/apes_seg_global-shapenet-200epochs.py # for segmentation using global-based downsampling
# using multiple GPUs
# command: bash utils/dist_train.sh cfg_file num_gpus
bash utils/dist_train.sh configs/apes/apes_cls_local-modelnet-200epochs.py 2 # for classification using local-based downsampling
bash utils/dist_train.sh configs/apes/apes_cls_global-modelnet-200epochs.py 2 # for classification using global-based downsampling
bash utils/dist_train.sh configs/apes/apes_seg_local-shapenet-200epochs.py 2 # for segmentation using local-based downsampling
bash utils/dist_train.sh configs/apes/apes_seg_global-shapenet-200epochs.py 2 # for segmentation using global-based downsampling
Test
Test model with checkpoint using:
# using single GPU
# command: bash utils/single_gpu_test.sh cfg_file ckpt_path
bash utils/single_gpu_test.sh configs/apes/apes_cls_local-modelnet-200epochs.py ckpt_path # for classification using local-based downsampling
bash utils/single_gpu_test.sh configs/apes/apes_cls_global-modelnet-200epochs.py ckpt_path # for classification using global-based downsampling
bash utils/single_gpu_test.sh configs/apes/apes_seg_local-shapenet-200epochs.py ckpt_path # for segmentation using local-based downsampling
bash utils/single_gpu_test.sh configs/apes/apes_seg_global-shapenet-200epochs.py ckpt_path # for segmentation using global-based downsampling
# using multiple GPUs
# command: bash utils/dist_test.sh cfg_file ckpt_path num_gpus
bash utils/dist_test.sh configs/apes/apes_cls_local-modelnet-200epochs.py ckpt_path 2 # for classification using local-based downsampling
bash utils/dist_test.sh configs/apes/apes_cls_global-modelnet-200epochs.py ckpt_path 2 # for classification using global-based downsampling
bash utils/dist_test.sh configs/apes/apes_seg_local-shapenet-200epochs.py ckpt_path 2 # for segmentation using local-based downsampling
bash utils/dist_test.sh configs/apes/apes_seg_global-shapenet-200epochs.py ckpt_path 2 # for segmentation using global-based downsampling
Visualization
Visualize results with checkpoint using:
# using single GPU
# command: bash utils/single_gpu_test.sh cfg_file ckpt_path -vis
bash utils/single_gpu_test.sh configs/apes/apes_cls_local-modelnet-200epochs.py ckpt_path -vis # for classification using local-based downsampling
bash utils/single_gpu_test.sh configs/apes/apes_cls_global-modelnet-200epochs.py ckpt_path -vis # for classification using global-based downsampling
bash utils/single_gpu_test.sh configs/apes/apes_seg_local-shapenet-200epochs.py ckpt_path -vis # for segmentation using local-based downsampling
bash utils/single_gpu_test.sh configs/apes/apes_seg_global-shapenet-200epochs.py ckpt_path -vis # for segmentation using global-based downsampling
# using multiple GPUs
# command: bash utils/dist_test.sh cfg_file ckpt_path num_gpus -vis
bash utils/dist_test.sh configs/apes/apes_cls_local-modelnet-200epochs.py ckpt_path 2 -vis # for classification using local-based downsampling
bash utils/dist_test.sh configs/apes/apes_cls_global-modelnet-200epochs.py ckpt_path 2 -vis # for classification using global-based downsampling
bash utils/dist_test.sh configs/apes/apes_seg_local-shapenet-200epochs.py ckpt_path 2 -vis # for segmentation using local-based downsampling
bash utils/dist_test.sh configs/apes/apes_seg_global-shapenet-200epochs.py ckpt_path 2 -vis # for segmentation using global-based downsampling
Citation
If you are interested in this work, please cite as below:
@inproceedings{wu_2023_attention,
author={Wu, Chengzhi and Zheng, Junwei and Pfrommer, Julius and Beyerer, J\"urgen},
title={Attention-Based Point Cloud Edge Sampling},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}