Consolidate Then Refine: 3D Object Detection with LiDAR and 4D Radar in Adverse Weather

Adverse weather degrades LiDAR and camera observations, whereas 4D radar remains relatively stable but suffers from limited spatial resolution. Existing LiDAR–radar detectors mainly fuse the two modalities at the scene level, which couples their representations and prevents modality-specific evidence from being fully exploited. We develop a consolidate-then-refine framework that preserves modality-specific representations and associates their evidence at the object level. This framework consolidates independently generated LiDAR and radar candidates into unified object hypotheses and progressively refines their representations and geometry. On the official K-Radar v2.1 benchmark, our detector achieves 66.0% 3D [email protected], surpassing the state of the art by 9.5 points.