Created by Hugues THOMAS

Introduction

This repository contains the implementation of Kernel Point Convolution (KPConv) in PyTorch.

KPConv is also available in Tensorflow (original but older implementation)

KPConv is a point convolution operator presented in our ICCV2019 paper (arXiv). If you find our work useful in your research, please consider citing:

@article{thomas2019KPConv,
    Author = {Thomas, Hugues and Qi, Charles R. and Deschaud, Jean-Emmanuel and Marcotegui, Beatriz and Goulette, Fran{\c{c}}ois and Guibas, Leonidas J.},
    Title = {KPConv: Flexible and Deformable Convolution for Point Clouds},
    Journal = {Proceedings of the IEEE International Conference on Computer Vision},
    Year = {2019}
}

Installation

This implementation has been tested on Ubuntu 18.04 and Windows 10. Details are provided in INSTALL.md.

Experiments

We provide scripts for three experiments: ModelNet40, S3DIS and SemanticKitti. The instructions to run these experiments are in the doc folder.

• Object Classification: Instructions to train KP-CNN on an object classification task (Modelnet40).

• Scene Segmentation: Instructions to train KP-FCNN on a scene segmentation task (S3DIS).

• SLAM Segmentation: Instructions to train KP-FCNN on a slam segmentation task (SemanticKitti).

• New Dataset: Instructions to train KPConv networks on your own data.

• Pretrained models: We provide pretrained weights and instructions to load them.

• Visualization scripts: For now only one visualization script has been implemented: the kernel deformations display.

TODO: Guide for these experiments

Acknowledgment

Our code uses the nanoflann library.

License

Our code is released under MIT License (see LICENSE file for details).

Updates

• 27/04/2020: Initial release.