LinStereo: Linear-Complexity Global Attention for Multi-Scale Iterative Stereo Matching

Existing Vision Foundation Model (VFM)-based iterativestereo pipelines under-exploit three information pathways: multi-scalebackbone features are collapsed into single-level correlations, geometricpriors remain untapped at initialization, and context propagates only lo-cally. These gaps widen under degraded photometric cues, making under-water scenes a stringent generalization test. To address this, we proposeLinStereo, built upon Depth Anything V3, whose core is a Position-Aware Linear Attention (PALA) module that replaces local recurrencewith global aggregation at linear cost, propagating reliable estimatesfrom well-matched regions into degraded areas while preserving dispar-ity structure. PALA is made effective by two enabling components: Hi-erarchical Semantic Cost Volumes (HSCV), which supply scale-alignedcorrelations from the VFM feature hierarchy, and a Depth Prior Ini-tialization (DPI) that converts monocular depth into a metrically cali-brated warm start. LinStereo achieves state-of-the-art-level accuracy onstandard benchmarks and strong cross-domain generalization, particu-larly on underwater scene where severe photometric degradation makesstereo matching particularly challenging, attaining the best overall ac-curacy with consistent gains (28% lower AbsRel on TartanAir-UW,26% on SQUID, a real-world underwater dataset). Code is availableat https://u7079256.github.io/LinStereo/.