Associatively Segmenting Instances and Semantics in Point Clouds

Xinlong Wang, Shu Liu, Xiaoyong Shen, Chunhua Shen, Jiaya Jia; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4096-4105

Abstract


A 3D point cloud describes the real scene precisely and intuitively. To date how to segment diversified elements in such an informative 3D scene is rarely discussed. In this paper, we first introduce a simple and flexible framework to segment instances and semantics in point clouds simultaneously. Then, we propose two approaches which make the two tasks take advantage of each other, leading to a win-win situation. Specifically, we make instance segmentation benefit from semantic segmentation through learning semantic-aware point-level instance embedding. Meanwhile, semantic features of the points belonging to the same instance are fused together to make more accurate per-point semantic predictions. Our method largely outperforms the state-of-the-art method in 3D instance segmentation along with a significant improvement in 3D semantic segmentation. Code has been made available at: https://github.com/WXinlong/ASIS.

Related Material


[pdf]
[bibtex]
@InProceedings{Wang_2019_CVPR,
author = {Wang, Xinlong and Liu, Shu and Shen, Xiaoyong and Shen, Chunhua and Jia, Jiaya},
title = {Associatively Segmenting Instances and Semantics in Point Clouds},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}