WCGNet: A Weather Codebook and Gating Fusion for Robust 3D Detection Under Adverse Conditions

Three-dimensional (3D) object detection constitutes a fundamental task in the field of environmental perception. While LiDAR provides high-precision 3D geometric data, its performance significantly degrades under adverse weather conditions like dense fog and heavy snow, where point cloud quality deteriorates. To address this challenge, WCGNet is proposed as a robust 3D detection framework that enhances feature representation against weather corruption. The framework introduces two key components: a Weather Codebook module and a Weather-Aware Gating Fusion module. The Weather Codebook, trained on paired clear and adverse weather scenes, learns to store clear-scene reference features, providing structural guidance for foggy scenarios. The Weather-Aware Gating Fusion module then integrates the degraded features with the codebook’s reference features through a spatial attention mechanism, a multi-head attention network, a gating mechanism, and a fusion module to dynamically recalibrate and combine features, thereby effectively restoring weather-robust representations. Additionally, a foggy point cloud dataset, nuScenes-fog, is constructed based on the nuScenes dataset. Systematic evaluations are conducted on nuScenes, nuScenes-fog, and the STF multi-weather dataset. Experimental results indicate that the proposed framework significantly enhances detection performance and generalization capability under challenging weather conditions, demonstrating strong adaptability across different weather scenarios.