Abstract
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 https://github.com/feihuzhang/LiDARSeg.
Original language | English |
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Title of host publication | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 |
Pages | 9448-9455 |
Number of pages | 8 |
ISBN (Electronic) | 9781728173955 |
DOIs | |
State | Published - May 2020 |
Event | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France Duration: May 31 2020 → Aug 31 2020 |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
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ISSN (Print) | 1050-4729 |
Conference
Conference | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 |
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Country/Territory | France |
City | Paris |
Period | 5/31/20 → 8/31/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Funding
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).
Funders | Funder number |
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Multidisciplinary University Research Initiative | EP/N019474/1 |
Multidisciplinary University Research Initiative | |
Engineering and Physical Sciences Research Council | EP/M013774/1 |
Engineering and Physical Sciences Research Council |
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Artificial Intelligence