Dense Semantic Bird-Eye-View Map Generation from Sparse LiDAR Point Clouds via Distribution-aware Feature Fusion

Abstract

Semantic scene understanding in bird-eye view (BEV) plays a crucial role in autonomous driving. A common approach to generating BEV maps from LiDAR point-cloud data involves constructing a pillar-level representation by projecting 3D point clouds onto a 2D plane. This process partially discards spatial geometric information, and produces sparse semantic maps. However, downstream tasks (e.g., trajectory planning and prediction), typically require dense grid-like semantic BEV maps rather than sparse segmentation outputs. To bridge this gap, we propose PointDenseBEV, an end-to-end, distribution-aware feature fusion framework. It takes as input sparse LiDAR point clouds and directly generates dense semantic BEV maps. Spatial geometric information and temporal context are embedded as auxiliary semantic cues within the BEV grid representation to enhance semantic density. Extensive experiments on the SemanticKITTI dataset demonstrate that our method achieves competitive performance compared to existing approaches.