BroadBEV: Collaborative LiDAR-camera Fusion for Broad-sighted Bird’s Eye View Map Construction
Year: 2024
Type: conference-paper
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.
Authors: Minsu Kim, Giseop Kim, Kyong Hwan Jin, Sunwook Choi
Institutions: Korea Institute of Science and Technology, Korea University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4401416341