README.md

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

Introduction

We implement PAConv and provide the result and checkpoints on S3DIS dataset.

@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={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3173--3182},
  year={2021}
}

Notice: The original PAConv paper used step learning rate schedule. We discovered that cosine schedule achieves slightly better results and adopt it in our implementations.

Results

S3DIS

Method	Split	Lr schd	Mem (GB)	Inf time (fps)	mIoU (Val set)	Download
PAConv (SSG)	Area_5	cosine 150e	5.8		66.65	model | log
PAConv* (SSG)	Area_5	cosine 200e	3.8		65.33	model | log

Notes:

• We use XYZ+Color+Normalized_XYZ as input in all the experiments on S3DIS datasets.
• Area_5 Split means training the model on Area_1, 2, 3, 4, 6 and testing on Area_5.
• PAConv* stands for the CUDA implementation of PAConv operations. See the paper appendix section D for more details. In our experiments, the training of PAConv* is found to be very unstable. We achieved slightly lower mIoU than the result in the paper, but is consistent with the result obtained by running their official code. Besides, although the GPU memory consumption of PAConv* is significantly lower than PAConv, its training and inference speed are actually slower (by ~10%).

Indeterminism

Since PAConv testing adopts sliding patch inference which involves random point sampling, and the test script uses fixed random seeds while the random seeds of validation in training are not fixed, the test results may be slightly different from the results reported above.