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@ -5,9 +5,9 @@ Created by Hugues THOMAS
## Introduction
This repository contains the implementation of **Kernel Point Convolution** (KPConv) in PyTorch. *[Work in progress]*
This repository contains the implementation of **Kernel Point Convolution** (KPConv) in [PyTorch](https://pytorch.org/).
KPConv is also available in [Tensorflow](https://github.com/HuguesTHOMAS/KPConv) (original implementation)
KPConv is also available in [Tensorflow](https://github.com/HuguesTHOMAS/KPConv) (original but older implementation)
KPConv is a point convolution operator presented in our ICCV2019 paper ([arXiv](https://arxiv.org/abs/1904.08889)). If you find our work useful in your
research, please consider citing:
@ -23,14 +23,31 @@ research, please consider citing:
## Installation
TODO
This implementation has been tested on Ubuntu 18.04 and Windows 10. Details are provided in [INSTALL.md](./INSTALL.md).
## Experiments
Currently only two experiments are available in Pytorch: classification on ModelNet40 and segmentation on S3DIS.
We provide scripts for three experiments: ModelNet40, S3DIS and SemanticKitti. The instructions to run these
experiments are in the [doc](./doc) folder.
TODO: Guide for runnig experiments
TODO: More experiments
* [Object Classification](./doc/object_classification_guide.md): Instructions to train KP-CNN on an object classification
task (Modelnet40).
* [Scene Segmentation](./doc/scene_segmentation_guide.md): Instructions to train KP-FCNN on a scene segmentation
task (S3DIS).
* [SLAM Segmentation](./doc/slam_segmentation_guide.md): Instructions to train KP-FCNN on a slam segmentation
task (SemanticKitti).
* [New Dataset](./doc/new_dataset_guide.md): Instructions to train KPConv networks on your own data.
* [Pretrained models](./doc/pretrained_models_guide.md): We provide pretrained weights and instructions to load them.
* [Visualization scripts](./doc/visualization_guide.md): For now only one visualization script has been implemented:
the kernel deformations display.
TODO: Guide for these experiments
## Acknowledgment
@ -40,4 +57,4 @@ Our code uses the <a href="https://github.com/jlblancoc/nanoflann">nanoflann</a>
Our code is released under MIT License (see LICENSE file for details).
## Updates
* 31/03/2020: Initial release.
* 27/04/2020: Initial release.