AquaStereo: Enabling Underwater Stereo Matching via Depth-Conditioned Diffusion and Geometry Self-Distillation

Learning-based stereo matching models struggle in under-water environments due to scarce in-domain data and the difficulty ofextracting discriminative correspondences from degraded imagery. In thiswork, we present AquaStereo, a perception-enhanced framework witha data simulation pipeline and a self-distillation strategy that jointly ad-dress data scarcity and feature degradation in underwater stereo match-ing. First, a depth-conditioned diffusion pipeline renders underwaterstereo pairs while preserving binocular geometry, with a lightweight left-right consistency module ensuring geometric alignment. Training on thissynthetic corpus effectively narrows the terrestrial–underwater gap andimproves zero-shot robustness. Second, a frozen binocular teacher trainedon clean terrestrial pairs guides a student exposed to rendered underwa-ter pairs with perturbations. A stage-weighted sequence loss is performedto align the student’s disparities with the teacher’s geometry, while aclean-branch supervision with shared pseudo targets prevents scale drift.To further enhance feature stability under turbidity and low texture,we introduce learnable perception frames, a perception-enhanced featureformulation that constructs robust matching descriptors by fusing tem-poral cues from two auxiliary views encoded by a video backbone withsemantic features extracted by a strong image encoder. Extensive experi-ments demonstrate that AquaStereo substantially improves robustnessand zero-shot generalization in challenging underwater scenarios.