A 4D radar and LiDAR fusion framework for weather-robust 3D object detection.

As a foundational technology in autonomous driving, 3D object detection plays a critical role by enabling vehicles to perceive their surroundings. However, LiDAR suffers from severe point cloud degradation in adverse weather, while 4D Radar, though more robust, introduces low-resolution and noisy measurements. Fusing LiDAR and 4D Radar is expected to enhance robustness, yet existing methods lack the ability to dynamically adapt to weather-induced sensor degradation and remain vulnerable to radar noise. To address these issues, we propose DDMDGF, a novel LiDAR-4D Radar fusion framework for weather-robust 3D object detection. The framework introduces a Semantic-guided Foreground-aware Denoising (SFD) module, which leverages enhanced semantic feature prediction and hybrid dynamic thresholding to suppress 4D radar noise while preserving critical foreground structures. In addition, we design a Multi-scale Dual-attention Gated Fusion (MDGF) module, which employs parallel intra-modal and inter-modal backbones to extract complementary features, and uses a channel-spatial joint attention gate to adaptively balance modality contributions under varying weather conditions. Extensive experiments on the VoD and K-Radar datasets demonstrate the effectiveness of the proposed method: DDMDGF achieves +7.3% AP3D and +4.9% APBEV improvements over L4DR on K-Radar, and under the most severe fog level in VoD-Fog, it outperforms L4DR by +1.6%, +1.8%, and +1.5% mAP on cars, pedestrians, and cyclists, respectively.