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Kernel Point Convolutions
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README.md

Intro figure

Created by Hugues THOMAS

Introduction

This is an alpha version of the code, more features will be added in the next weeks.

Update 03/05/2019, bug found with TF 1.13 and CUDA 10. We found an internal bug inside tf.matmul operation. It returns absurd values like 1e12, leading to the apparition of NaNs in our network. We advise to use the code with an older version of tensorflow (TF 1.12 works well).

Paper

arXiv

@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 = {arXiv preprint arXiv:1904.08889},
    Year = {2019}
}

Installation

A step-by-step installation guide for Ubuntu 16.04 is provided in INSTALL.md. Windows is currently not supported as the code uses tensorflow custom operations.

Experiments

We provide scripts for many experiments. The instructions to run these experiments are in the doc folder.

  • Object Classification: Instructions to train KP-CNN on an object classification task (Modelnet40).

  • Object Segmentation: Instructions to train KP-FCNN on an object segmentation task (ShapeNetPart)

  • Scene Segmentation: Instructions to train KP-FCNN on several scene segmentation tasks (S3DIS, Scannet, Semantic3D, NPM3D).

  • New Dataset: Instructions to train KPConv networks on your own data.

  • Visualization of learned features (TODO).

  • Visualization of learned kernel deformations (TODO).

  • Visualization of Effective Receptive Fields (TODO).

Acknowledgment

Our code uses the nanoflann library.

License

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

Updates

  • 19/04/2019: Initial release.
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