Abstract
Three-dimensional object detection based on the fusion of millimeter-wave radar and cameras is increasingly gaining attention due to characteristics of low cost, high accuracy, and strong robustness. Recently, the bird's eye view (BEV) fusion paradigm has dominated radar-camera fusion-based 3D object detection methods. In the BEV fusion paradigm, the detection accuracy is jointly determined by the precision of both image BEV features and radar BEV features. The precision of image BEV features is significantly influenced by depth estimation accuracy, whereas estimating depth from a monocular image is naturally a challenging, ill-posed problem. In this article, we propose a novel approach to enhance depth estimation accuracy by fusing camera perspective view (PV) features and radar perspective view features, thereby improving the precision of image BEV features. The refined image BEV features are then fused with radar BEV features to achieve more accurate 3D object detection results. To realize PV fusion, we designed a radar image generation module based on radar cross-section (RCS) and depth information, accurately projecting radar data into the camera view to generate radar images. The radar images are used to extract radar PV features. We present a cross-modal feature fusion module using the attention mechanism to dynamically fuse radar PV features with camera PV features. Comprehensive evaluations on the nuScenes 3D object detection dataset demonstrate that the proposed dual-view fusion paradigm outperforms the BEV fusion paradigm, achieving state-of-the-art performance with 64.2 NDS and 56.3 mAP.