Abstract
LiDAR point cloud compression is important for autonomous driving as it consumes a lot of storage and bandwidth. Although the fusion of camera and LiDAR for vision perception has been well studied, it remains unexplored that how we can improve the compression of LiDAR point cloud data using cross-modal information from cameras. In this paper’ we propose a multi-modality compression framework for LiDAR point cloud by exploiting the depth information predicted from its paired image. To the best of our knowledge’ our model is the first multi-modality compression framework for point cloud. Specifically’ we first represent point cloud based on octrees to reduce spatial redundancy. Then’ we propose a cross-modal fusion structure to improve the compression of these octrees’ with depth distribution extracted from the camera pixels and acts as side information. Compared to previous state-of-the-art (SOTA) method, our approach obtains up to 8.10% compression rate gain for LiDAR point cloud compression.
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 callDisclaimer: 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.
