Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications

arXiv:2404.06165v1 Announce Type: new
Abstract: Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors. The typical extracted radar point cloud is 2D without height information due to insufficient antennas along the elevation axis, which challenges the network performance. This work introduces a learning-based approach to infer the height of radar points associated with 3D objects. A novel robust regression loss is introduced to address the sparse target challenge. In addition, a multi-task training …

abstract applications arxiv challenges cloud cs.cv cs.mm data eess.iv eess.sp fusion information multi-task learning network perception performance radar robustness sensor sensors tasks type via work