WeatherMamba: Reliable Geometry-Aware Generalization for Adverse-Weather LiDAR Sensor Point-Cloud Semantic Segmentation

Abstract

Adverse weather degrades LiDAR semantic segmentation by altering return density, measured intensity, local geometry, and feature reliability. We study source-only domain generalization from clear-weather or synthetic training data to unseen real adverse-weather scenes. WeatherMamba is a geometry- and reliability-aware state-space framework that integrates multiscale neighborhood aggregation, reliability-conditioned feature refinement, and efficient long-range sequence modeling. Its central component, Weather-Guided Reliability Gating (WGRG), derives a continuous point-wise gate from conditionally normalized measured intensity, multiscale local density, and latent features. This gate bounds residual corrections without an externally supplied weather type, prior target-reflectance values, or deployment-time meteorological measurements. A controlled pseudo-weather strategy models signal perturbation and point-support loss separately during source-domain training. WeatherMamba achieves 37.4% mIoU on SemanticKITTI→SemanticSTF and 22.8% on SynLiDAR→SemanticSTF, ranking third overall while attaining the highest class IoU in five classes under each protocol. In the single-seed module ablation, the complete configuration improves the module-free baseline from 29.4% to 37.4%. Relative to PTv3 with Modules, WeatherMamba reduces parameters, FLOPs, latency, and peak allocated memory by 19.1%, 34.8%, 16.5%, and 25.2%, respectively, with a 0.4-point reduction in mIoU. These results indicate a competitive accuracy–efficiency trade-off under the evaluated protocols.