GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection

arXiv:2403.11848v1 Announce Type: new
Abstract: Integrating LiDAR and camera information into Bird's-Eye-View (BEV) representation has emerged as a crucial aspect of 3D object detection in autonomous driving. However, existing methods are susceptible to the inaccurate calibration relationship between LiDAR and the camera sensor. Such inaccuracies result in errors in depth estimation for the camera branch, ultimately causing misalignment between LiDAR and camera BEV features. In this work, we propose a robust fusion framework called Graph BEV. Addressing errors caused by …

3d object 3d object detection abstract alignment arxiv autonomous autonomous driving bird cs.cv detection driving errors feature however information lidar modal multi-modal object relationship representation robust sensor type view