kpconv_torch

Created by Hugues THOMAS

Introduction

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

Another implementation of KPConv is available in PyTorch-Points-3D

Introduction

KPConv is a point convolution operator presented in the Hugues Thomas's ICCV2019 paper (arXiv). 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

Scripts for three experiments are provided (ModelNet40, S3DIS and SemanticKitti). The instructions to run these experiments are in the doc folder.

⚠️ Disclaimer: in this repo version, we only maintain the S3DIS material regarding Scene Segmentation. Instructions to train KP-FCNN on a scene segmentation task (S3DIS) can be found in the doc.

As a bonus, a visualization scripts has been implemented: the kernel deformations display.

Acknowledgment

Initial tribute to Hugues Thomas, this repo is a fork of KPConv-PyTorch repo.

The code uses the nanoflann library.

License

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