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Instance segmentation of LiDAR point clouds

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...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA40945.2020.9196622
Publication website:
https://ieeexplore.ieee.org/xpl/conhome/9187508/proceeding

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Name:
European Commission
Grant:
321162
Publisher:
IEEE
Host title:
2020 IEEE International Conference on Robotics and Automation (ICRA)
Pages:
9448-9455
Publication date:
2020-09-15
Acceptance date:
2020-01-22
Event title:
International Conference on Robotics and Automation 2020
Event location:
Virtual event
Event website:
https://www.icra2020.org/
Event start date:
2020-05-31
Event end date:
2020-08-31
DOI:
EISSN:
2577-087X
EISBN:
978-1-7281-7395-5
Language:
English
Keywords:
Pubs id:
1125113
Local pid:
pubs:1125113
Deposit date:
2020-08-11

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