Radar has gained much attention in autonomous driving due to its accessibility and robustness. However, its standalone application for depth perception is constrained by issues of sparsity and noise. Radar-camera depth estimation offers a more promising complementary solution. Despite significant progress, current approaches fail to produce satisfactory dense depth maps, due to the unsatisfactory processing of the sparse and noisy radar data. They constrain the regions of interest for radar points in rigid rectangular regions, which may introduce unexpected errors and confusions. To address these issues, we develop a structure-aware strategy for radar depth enhancement, which provides more targeted regions of interest by leveraging the structural priors of RGB images. Furthermore, we design a Multi-Scale Structure Guided Network to enhance radar features and preserve detailed structures, achieving accurate and structure-detailed dense metric depth estimation. Building on these, we propose a structure-aware radar-camera depth estimation framework, named SA-RCD. Extensive experiments demonstrate that our SA-RCD achieves state-of-the-art performance on the nuScenes dataset. Our code will be available at https://github.com/FreyZhangYeh/SA-RCD.

4D millimeter-wave radar plays a pivotal role in autonomous driving due to its cost-effectiveness and robustness in adverse weather. However, the application of 4D radar point cloud in 3D perception tasks is hindered by its inherent sparsity and noise. To address these challenges, we propose LGDD, a novel local-global synergistic dual-branch 3D object detection framework using 4D radar. Specifically, we first introduce a point-based branch, which utilize a voxel-attended point feature extractor (VPE) to integrate semantic segmentation with cluster voting, thereby mitigating radar noise and extracting local-clustered instances features. Then, for the conventional pillar-based branch, we design a query-based feature pre-fusion (QFP) to address the sparsity and enhance global context representation. Additionally, we devise a proposal mask to filter out noisy points, enabling more focused clustering on regions of interest. Finally, we align the local instances with global context through semantics-geometry aware fusion (SGF) module to achieve comprehensive scene understanding. Extensive experiments demonstrate that LGDD achieves state-of-the-art performance on the public View-of-Delft and TJ4DRadSet datasets. Source code is available at https://github.com/shawnnnkb/LGDD.