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PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

by Mutian Xu*, Runyu Ding*, Hengshuang Zhao, and Xiaojuan Qi.

Introduction

This repository is built for the official implementation of:

PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds (CVPR2021) [arXiv]

If you find our work useful in your research, please consider citing:

@inproceedings{xu2021paconv,
  title={PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds},
  author={Xu, Mutian and Ding, Runyu and Zhao, Hengshuang and Qi, Xiaojuan},
  booktitle={CVPR},
  year={2021}
}

Highlight

  • All initialization models and trained models are available.
  • Provide fast multiprocessing training (nn.parallel.DistributedDataParallel) with official nn.SyncBatchNorm.
  • Incorporated with tensorboardX for better visualization of the whole training process.
  • Support recent versions of PyTorch.
  • Well designed code structures for easy reading and using.

Usage

We provide scripts for different point cloud processing tasks:

You can find the instructions for running these tasks in the above corresponding folders.

Performance

The following tables report the current performances on different tasks and datasets. ( * denotes the backbone architectures)

Object Classification on ModelNet40

Method OA
PAConv (*PointNet) 93.2%
PAConv (*DGCNN) 93.9%

Shape Part Segmentation on ShapeNet Part

Method Class mIoU Instance mIoU
PAConv (*DGCNN) 84.6% 86.1%

Indoor Scene Segmentation on S3DIS Area-5

Method S3DIS mIoU
PAConv (*PointNet++) 66.58%

Contact

You are welcome to send pull requests or share some ideas with us. Contact information: Mutian Xu ([email protected]) or Runyu Ding ([email protected]).

Acknowledgement

Our code base is partially borrowed from PointWeb, DGCNN and PointNet++.