KPConv-PyTorch/README.md

![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](https://pytorch.org/).

KPConv is also available in [Tensorflow](https://github.com/HuguesTHOMAS/KPConv) (original but older 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

This implementation has been tested on Ubuntu 18.04 and Windows 10. Details are provided in [INSTALL.md](./INSTALL.md).


## Experiments

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

* [Object Classification](./doc/object_classification_guide.md): Instructions to train KP-CNN on an object classification
 task (Modelnet40).
 
* [Scene Segmentation](./doc/scene_segmentation_guide.md): Instructions to train KP-FCNN on a scene segmentation 
 task (S3DIS).
 
* [SLAM Segmentation](./doc/slam_segmentation_guide.md): Instructions to train KP-FCNN on a slam segmentation 
 task (SemanticKitti).
 
* [New Dataset](./doc/new_dataset_guide.md): Instructions to train KPConv networks on your own data.
 
* [Pretrained models](./doc/pretrained_models_guide.md): We provide pretrained weights and instructions to load them.
 
* [Visualization scripts](./doc/visualization_guide.md): For now only one visualization script has been implemented: 
the kernel deformations display.

TODO: Guide for these 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
* 27/04/2020: Initial release.