From c303bcbbce0bbb216f5d54b5e7b1cbb8c9270ad7 Mon Sep 17 00:00:00 2001 From: HuguesTHOMAS Date: Mon, 27 Apr 2020 18:18:00 -0400 Subject: [PATCH] Corrections --- README.md | 31 ++++++++++++++++++++++++------- 1 file changed, 24 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 210af0a..671e353 100644 --- a/README.md +++ b/README.md @@ -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 nanoflann Our code is released under MIT License (see LICENSE file for details). ## Updates -* 31/03/2020: Initial release. +* 27/04/2020: Initial release.