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Adaptive Graph Convolution for Point Cloud Analysis

example

This repository contains the implementation of AdaptConv for point cloud analysis.

Adaptive Graph Convolution (AdaptConv) is a point cloud convolution operator presented in our ICCV2021 paper. If you find our work useful in your research, please cite our paper.

preprint:

@article{zhou2021adaptive,
  title={Adaptive Graph Convolution for Point Cloud Analysis},
  author={Zhou, Haoran and Feng, Yidan and Fang, Mingsheng and Wei, Mingqiang and Qin, Jing and Lu, Tong},
  journal={arXiv preprint arXiv:2108.08035},
  year={2021}
}

Installation

  • The code has been tested on one configuration:

    • PyTorch 1.1.0, CUDA 10.1

  • Install required packages:

    • numpy
    • h5py
    • scikit-learn
    • matplotlib

Classification

classification.md

Part Segmentation

part_segmentation.md

Indoor Segmentation

sem_segmentation.md

Updates

  • 09/30/2021: Updated code for part segmentation.
  • 09/30/2021: Added code for S3DIS indoor segmentation.