Reliability-Aware Temporal Multimodal Fusion of LiDAR and 4D Radar for Adverse-Weather 3D Object Detection

Robust 3D object detection under adverse weather conditions is critical for safe autonomous driving. Weather-robust 4D Radar can effectively mitigate challenges such as the loss of foreground points that occur with LiDAR under adverse weather conditions. Nevertheless, 4D Radar suffers from limitations such as low resolution, which makes LiDAR and 4D Radar fusion still struggle to fully observe targets under adverse weather conditions in individual frames. To address the limitations of individual observations, this work presents the first multi-frame temporal information fusion for LiDAR-4D Radar 3D object detection in adverse weather. However, multi-frame temporal fusion of LiDAR and 4D Radar in adverse weather faces two key challenges: the accumulation of temporal noises and unreliable historical observations. Consequently, naive temporal fusion of LiDAR and 4D Radar brings little or no gain and can even underperform the single-frame baseline. To address these challenges, we propose TMF, a multi-frame temporal fusion framework for adverse-weather 3D object detection. First, instead of treating all frames as equally valid evidence, the Temporal Radar Denoising (TRD) block performs reliability-aware temporal reweighting, discounting clutter at aggregation time so that noise no longer accumulates across frames. Subsequently, the Radar-Guided Temporal Compensation (RGTC) block performs sparsity-guided cross-modal retrieval, querying historical LiDAR features where 4D Radar reveals missing observations, and the Degradation-aware Gated Prior Fusion (DGPF) block gates the retrieved features against the current frame to focus on the important information. Extensive experiments on K-Radar dataset demonstrate consistent improvements across weather conditions, including an average gain of 6.49% AP 3D in adverse weather.