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LightConvPoint

A framework to build convolutional network for point cloud processing.

Paper: FKAConv, Feature-Kernel Alignment for Point Cloud Convolution

LightConvPoint is the framework developped and used for FKAConv experiments. The paper is available at openaccess.thecvf.com

If you use the FKAConv code or the LightConvPoint framework in your research, please consider citing:

@inproceedings{boulch2020fka,
  title={{FKAConv: Feature-Kernel Alignment for Point Cloud Convolution}},
  author={Boulch, Alexandre and Puy, Gilles and Marlet, Renaud},
  booktitle={15th Asian Conference on Computer Vision (ACCV 2020)},
  year={2020}
}

Code for FKAConv

The complete code and examples for FKAConv is available at https://github.com/valeoai/FKAConv. It relies on LightConvPoint v0.2.

Ressources

• 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).

Example

We provide examples classification and segmentation datasets:

• ModelNet40
• ShapeNet
• S3DIS
• Semantic8
• NPM3D