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

TL;DR

BroadBEV introduces a novel sensor fusion method that enhances bird's eye view map construction by improving camera BEV estimation and LiDAR data completion through spatial synchronization, Point-scattering, and ColFusion techniques.

Contribution

It proposes a new fusion framework that addresses camera BEV inaccuracies and LiDAR sparsity, significantly improving broad-sighted BEV perception.

Findings

Achieves superior BEV perception accuracy.

Enhances depth estimation in camera branches.

Demonstrates significant performance improvements.

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 broad BEV fusion (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.