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.
|Title of host publication||2020 IEEE International Conference on Robotics and Automation, ICRA 2020|
|Number of pages||8|
|State||Published - May 2020|
|Event||2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France|
Duration: May 31 2020 → Aug 31 2020
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Conference||2020 IEEE International Conference on Robotics and Automation, ICRA 2020|
|Period||5/31/20 → 8/31/20|
Bibliographical noteFunding 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).
© 2020 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering