Geometry-Conditioned Shared-Slice Fusion and Gaussian Set Refinement for Adverse-Weather 3D Detection

Gated cameras provide range-selective observations through distinct temporal integration windows, offering complementary cues to LiDAR and RGB for 3D object detection in adverse weather. Existing multimodal fusion methods such as SAMFusion, however, commonly encode the near, middle, and far gated measurements as pseudo-RGB channels, conflating temporally distinct observations and obscuring their range-selective responses. This representation-level limitation is compounded at inference, where conventional suppression determines redundancy from confidence and geometric overlap without accounting for the physical evidence supporting each prediction. We present a representation-to-set framework that addresses both deficiencies. Geometry-Conditioned Shared-Slice Aggregation (GCSA) processes the gated measurements through independent passes of a shared visual encoder and estimates spatial slice contributions from projected depth, projection validity, and cross-view consistency. Slice-Geometry-Conditioned Gaussian Set Refinement (SG-GSR) subsequently derives candidate reliability and pairwise compatibility from gated-slice agreement, local LiDAR support, and depth consistency to modulate same-class Gaussian score decay. SG-GSR preserves box geometry and class labels and requires no additional detector training. Compared with SAMFusion, the complete framework improves condition-specific Overall 3D AP${}_{40}$ by 2.22–4.50 points across daytime, nighttime, snow, and fog, with gains across all three distance intervals.