Abstract
This paper proposes a computationally fast and accurate framework to identify natively in 3D point clouds from LIDAR for autonomous driving. Speed is significant and detection is an indispensable component of safety. Many of the approaches are. However, most of them remain massive computation due to high dimensionality and densely of point clouds. We therefore investigate and carefully design an enhanced single-stage keypoint 3D multi-object detection framework to balance high accuracy and real-time efficiency. Experiments on the challenging KITTI dataset show that AEC3D outperforms other projection-based performances by a noteworthy margin. In addition, this is an embedded system-friendly approach, the inference times on TITAN Xp and Jetson AGX Xavier Developer Kit are 48 FPS and 14 FPS, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Intelligent Transportation Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
