SG-RoadSeg: End-to-End Collision-Free Space Detection Sharing Encoder Representations Jointly Learned via Unsupervised Deep Stereo-Reference-Cited by-同舟云学术

SG-RoadSeg: End-to-End Collision-Free Space Detection Sharing Encoder Representations Jointly Learned via Unsupervised Deep Stereo

Published:2024-05-13 Issue: Volume: Page:11063-11069
ISSN:
Container-title:2024 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Wu Zhiyuan¹,Li Jiaqi¹,Feng Yi¹,Liu Chengju¹,Ye Wei¹,Chen Qijun¹,Fan Rui¹

Affiliation:

1. Tongji University,College of Electronics & Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, the State Key Laboratory of Intelligent Autonomous Systems, and Frontiers Science Center for Intelligent Autonomous Systems,Shanghai,P. R. China,201804

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609961/10609862/10611191.pdf?arnumber=10611191

Reference42 articles.

1. SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection

2. Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving

3. SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection

4. Freespace Optical Flow Modeling for Automated Driving

5. Road Damage Detection Based on Unsupervised Disparity Map Segmentation

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