BroadBEV: Collaborative LiDAR-camera Fusion for Broad-sighted Bird's Eye View Map Construction

Minsu Kim, Giseop Kim, Kyong Hwan Jin, Sunwook Choi

Abstract: A recent sensor fusion in a Bird’s Eye View (BEV) space has shown its utility in various tasks such as 3D detection, map segmentation, etc. However, the approach struggles with inaccurate camera BEV estimation, and a perception of distant areas due to the sparsity of LiDAR points. In this paper, we propose a BEV fusion (BroadBEV) that aims to enhance camera BEV estimation for broad perception in the pre-defined BEV range, while simultaneously improving the completion of LiDAR’s sparsity in the entire BEV space. Toward that end, we devise Point-scattering that scatters LiDAR BEV distribution to camera depth distribution. The method boosts the learning of depth estimation of the camera branch and induces accurate location of dense camera features in BEV space. For an effective BEV fusion between the spatially synchronized features, we suggest ColFusion that applies self-attention weights of LiDAR and camera BEV features to each other. Our extensive experiments demonstrate that the suggested methods enable a broad BEV perception with remarkable performance gains.