End-to-end 3D point cloud instance segmentation without detection

Haiyong Jiang, Feilong Yan, Jianfei Cai, Jianmin Zheng, Jun Xiao

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

3D instance segmentation plays a predominant role in environment perception of robotics and augmented reality. Many deep learning based methods have been presented recently for this task. These methods rely on either a detection branch to propose objects or a grouping step to assemble same-instance points. However, detection based methods do not ensure a consistent instance label for each point, while the grouping step requires parameter-tuning and is computationally expensive. In this paper, we introduce an assign-and-suppress network, dubbed as AS-Net, to enable end-to-end instance segmentation without detection and a separate step of grouping. The core idea is to frame instance segmentation as a candidate assignment problem. At first, a set of instance candidates are sampled. Then we propose an assignment module for candidate assignment and a suppression module to eliminate redundant candidates. A mapping between instance labels and instance candidates is further sought to construct an instance grouping loss for the network training. Experimental results demonstrate that our method is more effective and efficient than previous detection-free approaches.

Original languageEnglish
Title of host publicationProceedings - 33th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2020
EditorsCe Liu, Greg Mori, Kate Saenko, Silvio Savarese
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages12793-12802
Number of pages10
ISBN (Electronic)9781728171685
ISBN (Print)9781728171692
DOIs
Publication statusPublished - 2020
EventIEEE Conference on Computer Vision and Pattern Recognition 2020 - Virtual, China
Duration: 14 Jun 202019 Jun 2020
http://cvpr2020.thecvf.com (Website )
https://openaccess.thecvf.com/CVPR2020 (Proceedings)
https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2020
Abbreviated titleCVPR 2020
CountryChina
CityVirtual
Period14/06/2019/06/20
Internet address

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