毫米波雷达物理先验引导的多模态3D目标检测OA

Multimodal 3D object detection has emerged as an effective solution to overcome the limitations of single sensors under adverse weather and lighting conditions.However,existing approaches are hindered by sparse radar point clouds,inaccurate image depth estimation,and weak cross-modal feature interaction.To address these challenges,we propose a radar prior-guided multimodal fusion framework.This framework constructs a radar prior enhancement network(RaPENet)which leverages physical attributes such as Radar Cross Section to densify sparse point clouds through dynamic Gaussian expansion and to enhance image depth estimation with spatially aware constraints.To further improve fusion in Bird's-Eye View(BEV)space,we design a Deformable Cross-Attention with Gated Fusion(DCAGFusion)module that enables spatially aligned and confidence-adaptive integration of cross-modal BEV features.Experiments on the nuScenes benchmark show that the proposed method achieves 57.4%NDS and 45.9%mAP,surpassing baseline models by 0.6%.These results highlight the advantage of incorporating radar physical priors and adaptive fusion for robust and accurate multimodal 3D detection in challenging environments.