| cpp_wrappers | ||
| datasets | ||
| kernels | ||
| models | ||
| utils | ||
| plot_convergence.py | ||
| README.md | ||
| test_models.py | ||
| train_ModelNet40.py | ||
| train_S3DIS.py | ||
| train_SemanticKitti.py | ||
| visualize_deformations.py | ||
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 (original 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
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 nanoflann library.
License
Our code is released under MIT License (see LICENSE file for details).
Updates
- 31/03/2020: Initial release.