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
broad BEV fusion (\textit{BroadBEV}) that addresses the problems with a spatial
synchronization approach of cross-modality. Our strategy aims to enhance camera
BEV estimation for a broad-sighted perception 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 BroadBEV provides a
broad-sighted BEV perception with remarkable performance gains.

提出了一种空间同步的跨模态方法，通过点散射将 LiDAR BEV 分布散布到相机深度分布，增强相机 BEV 估计和精确定位，同时改善整个 BEV 空间中 LiDAR 的稀疏性，实现了广视野 BEV 感知。