61 lines
2.3 KiB
Markdown
61 lines
2.3 KiB
Markdown
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Created by Hugues THOMAS
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## Introduction
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This repository contains the implementation of **Kernel Point Convolution** (KPConv) in [PyTorch](https://pytorch.org/).
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KPConv is also available in [Tensorflow](https://github.com/HuguesTHOMAS/KPConv) (original but older implementation)
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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
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research, please consider citing:
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```
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@article{thomas2019KPConv,
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Author = {Thomas, Hugues and Qi, Charles R. and Deschaud, Jean-Emmanuel and Marcotegui, Beatriz and Goulette, Fran{\c{c}}ois and Guibas, Leonidas J.},
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Title = {KPConv: Flexible and Deformable Convolution for Point Clouds},
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Journal = {Proceedings of the IEEE International Conference on Computer Vision},
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Year = {2019}
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}
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```
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## Installation
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This implementation has been tested on Ubuntu 18.04 and Windows 10. Details are provided in [INSTALL.md](./INSTALL.md).
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## Experiments
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We provide scripts for three experiments: ModelNet40, S3DIS and SemanticKitti. The instructions to run these
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experiments are in the [doc](./doc) folder.
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* [Object Classification](./doc/object_classification_guide.md): Instructions to train KP-CNN on an object classification
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task (Modelnet40).
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* [Scene Segmentation](./doc/scene_segmentation_guide.md): Instructions to train KP-FCNN on a scene segmentation
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task (S3DIS).
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* [SLAM Segmentation](./doc/slam_segmentation_guide.md): Instructions to train KP-FCNN on a slam segmentation
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task (SemanticKitti).
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* [New Dataset](./doc/new_dataset_guide.md): Instructions to train KPConv networks on your own data.
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* [Pretrained models](./doc/pretrained_models_guide.md): We provide pretrained weights and instructions to load them.
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* [Visualization scripts](./doc/visualization_guide.md): For now only one visualization script has been implemented:
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the kernel deformations display.
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TODO: Guide for these experiments
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## Acknowledgment
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Our code uses the <a href="https://github.com/jlblancoc/nanoflann">nanoflann</a> library.
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## License
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Our code is released under MIT License (see LICENSE file for details).
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## Updates
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* 27/04/2020: Initial release.
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