  LiDAR-based vision systems are integral for 3D object detection, which is
crucial for autonomous navigation. However, they suffer from performance
degradation in adverse weather conditions due to the quality deterioration of
LiDAR point clouds. Fusing LiDAR with the weather-robust 4D radar sensor is
expected to solve this problem. However, the fusion of LiDAR and 4D radar is
challenging because they differ significantly in terms of data quality and the
degree of degradation in adverse weather. To address these issues, we introduce
L4DR, a weather-robust 3D object detection method that effectively achieves
LiDAR and 4D Radar fusion. Our L4DR includes Multi-Modal Encoding (MME) and
Foreground-Aware Denoising (FAD) technique to reconcile sensor gaps, which is
the first exploration of the complementarity of early fusion between LiDAR and
4D radar. Additionally, we design an Inter-Modal and Intra-Modal ({IM}2 )
parallel feature extraction backbone coupled with a Multi-Scale Gated Fusion
(MSGF) module to counteract the varying degrees of sensor degradation under
adverse weather conditions. Experimental evaluation on a VoD dataset with
simulated fog proves that L4DR is more adaptable to changing weather
conditions. It delivers a significant performance increase under different fog
levels, improving the 3D mAP by up to 18.17% over the traditional LiDAR-only
approach. Moreover, the results on the K-Radar dataset validate the consistent
performance improvement of L4DR in real-world adverse weather conditions.
