![Intro figure](https://github.com/HuguesTHOMAS/KPConv/blob/master/doc/Github_intro.png)

Created by Hugues THOMAS

## Introduction

This repository contains the implementation of **Kernel Point Convolution** (KPConv) in PyTorch. *[Work in progress]*

KPConv is also available in [Tensorflow](https://github.com/HuguesTHOMAS/KPConv) (original implementation)
 
KPConv is a point convolution operator presented in our ICCV2019 paper ([arXiv](https://arxiv.org/abs/1904.08889)). 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

TODO

## Experiments

Currently only two experiments are available in Pytorch: classification on ModelNet40 and segmentation on S3DIS.

TODO: Guide for runnig experiments
TODO: More experiments

## Acknowledgment

Our code uses the <a href="https://github.com/jlblancoc/nanoflann">nanoflann</a> library.

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

## Updates
* 31/03/2020: Initial release.