A framework to build convolutional network for point cloud processing.
LightConvPoint is the framework developped and used for FKAConv experiments. The paper is available at arxiv: https://arxiv.org/abs/2004.04462
If you use the FKAConv code or the LightConvPoint framework in your research, please consider citing:
@article{boulch2020lightconvpoint,
title={{FKAConv: Feature-Kernel Alignment for Point Cloud Convolution}},
author={Boulch, Alexandre and Puy, Gilles and Marlet, Renaud},
journal={arXiv preprint arXiv:2004.04462},
year={2020}
}
- Installation: install and setup lightconvpoint
- Run experients: re-run experiments from the paper
- Getting started: start to design your own network
- Library features and implemented algorithms: description of avalailable algorithms in LCP (convolutional layers such as LightConvPoint or ConvPoint; support point selection including quantized search or farthest point sampling).
We provide examples classification and segmentation datasets:
- ModelNet40
- ShapeNet
- S3DIS (to be released)
- Semantic8 (to be released)
- NPM3D (to be released)