Rethinking Range View Representation for LiDAR Segmentation
Author:
Affiliation:
1. Shanghai AI Laboratory
2. ShanghaiTech University
3. The Chinese University of Hong Kong
4. Nanyang Technological University,S-Lab
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10376473/10376477/10376983.pdf?arnumber=10376983
Reference81 articles.
1. SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving
2. Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds
3. 3D-MiniNet: Learning a 2D Representation From Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation
4. RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
5. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
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