BEVPointNet3D: Fusing Bird's Eye View and Point Cloud Features for Robust 3D Lane Detection

Abstract

This paper introduces BEVPointNet3D, an innovative 3D lane detection model that effectively integrates Bird’s Eye View (BEV) and point cloud features. The proposed approach addresses the inherent limitations of conventional methods that predominantly rely on the flat-ground assumption. BEVPointNet3D incorporates a 2D encoder to extract preliminary lane information from BEV images. For 3D feature extraction, the model employs a hierarchical local-to-global processing scheme to capture the geometric characteristics of LiDAR point clouds. A novel cross-attention mechanism is implemented to precisely align and integrate the 2D and 3D feature representations. This architectural design not only improves detection accuracy but also strengthens the adaptability and performance of the model in complex driving scenarios. Comprehensive evaluations on the K-Lane and CampusLane datasets demonstrate the superior performance of BEVPoint-Net3D. Notably, the model exhibits exceptional capability in accurately estimating lane spatial positions on steep inclines, thereby providing reliable support for autonomous driving systems in challenging terrain conditions.