BEV-CAM3D: A Unified Bird’s-Eye View Architecture for Autonomous Driving with Monocular Cameras and 3D Point Clouds

Three-dimensional (3D) visual perception is pivotal for understanding surrounding environments in applications such as autonomous driving and mobile robotics. While LiDAR-based models dominate due to accurate depth sensing, their cost and sparse outputs have driven interest in camera-based systems. However, challenges like cross-domain degradation and depth estimation inaccuracies persist. This paper introduces BEVCAM3D, a unified bird’s-eye view (BEV) architecture that fuses monocular cameras and LiDAR point clouds to overcome single-sensor limitations. BEVCAM3D integrates a deformable cross-modality attention module for feature alignment and a fast ground segmentation algorithm to reduce computational overhead by 40%. Evaluated on the nuScenes dataset, BEVCAM3D achieves state-of-the-art performance, with a 73.9% mAP and a 76.2% NDS, outperforming existing LiDAR-camera fusion methods like SparseFusion (72.0% mAP) and IS-Fusion (73.0% mAP). Notably, it excels in detecting pedestrians (91.0% AP) and traffic cones (89.9% AP), addressing the class imbalance in autonomous driving scenarios. The framework supports real-time inference at 11.2 FPS with an EfficientDet-B3 backbone and demonstrates robustness under low-light conditions (62.3% nighttime mAP).