hongwang01 / RandLA-Net

RandLA-Net in Tensorflow

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, Andrew Markham. arXiv:1911.11236, 2019. RandLA-Net in Tensorflow, coming soon

Introduction

This repository contains the implementation of RandLA-Net, a simple and efficient neural architecture for semantic segmentation of large-scale 3D point clouds.

The following figure shows the basic building block of our RandLA-Net:

Qualitative Results

S3DIS

Semantic3D

SemanticKITTI

Quantitative Results

Semantic3D

Quantitative results of different approaches on Semantic3D (reduced-8). Only the recent published approaches are compared. Accessed on 15 November 2019.

SemanticKITTI

Quantitative results of different approaches on SemanticKITTI dataset.

S3DIS

Quantitative results of different approaches on S3DIS dataset.

Demo

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Citation

If you find our work useful in your research, please consider citing:

@article{hu2019randla,
  title={RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds},
  author={Hu, Qingyong and Yang, Bo and Xie, Linhai and Rosa, Stefano and Guo, Yulan and Wang, Zhihua and Trigoni, Niki and Markham, Andrew},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}