Instance Segmentation of LiDAR Point Clouds

Feihu Zhang, Chenye Guan, Jin Fang, Song Bai, Ruigang Yang, Philip H.S. Torr, Victor Prisacariu

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

19 Scopus citations


We propose a robust baseline method for instance segmentation which are specially designed for large-scale outdoor LiDAR point clouds. Our method includes a novel dense feature encoding technique, allowing the localization and segmentation of small, far-away objects, a simple but effective solution for single-shot instance prediction and effective strategies for handling severe class imbalances. Since there is no public dataset for the study of LiDAR instance segmentation, we also build a new publicly available LiDAR point cloud dataset to include both precise 3D bounding box and point-wise labels for instance segmentation, while still being about 3∼20 times as large as other existing LiDAR datasets. The dataset will be published at

Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Number of pages8
ISBN (Electronic)9781728173955
StatePublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: May 31 2020Aug 31 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT Research is mainly supported by Baidu’s Robotics and Auto-driving Lab, in part by the ERC grant ERC-2012-AdG 321162-HELIOS, EPSRC grant Seebibyte EP/M013774/1 and EPSRC/MURI grant EP/N019474/1. We would also like to acknowledge the Royal Academy of Engineering. Victor Adrian Prisacariu would like to thank the European Commission Project Multiple-actOrs Virtual Empathic CARegiver for the Elder (MoveCare).

Publisher Copyright:
© 2020 IEEE.

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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