SW-Mamba: Selective Weather Forgetting for Self-Supervised Monocular Depth Estimation

Self-supervised monocular depth estimation relies on cross-frame photometric consistency, an assumption severely challenged by adverse weather where appearance changes deviate from scene geometry. To address these challenges, we propose Selective Weather-forgetting Mamba (SW-Mamba), which formulates robust adverse-weather depth estimation as selective latent-state correction. Rather than treating inter-frame variation as a weather mask, SW-Mamba interprets temporal cues jointly with multidirectionally propagated spatial context to estimate a spatiotemporal correction gate. The gate modulates reference-guided state correction, selectively updating unreliable responses while preserving geometric consistency. During paired training, aligned clean states provide supervision for gate calibration and serve as structural references. At inference, an internal structural proxy enables deployment without aligned clean observations. Extensive experiments on WeatherKITTI, nuScenes, and DrivingStereo show consistent gains under adverse weather and strong cross-domain generalization.