LinStereo: Linear-Complexity Global Attention for Multi-Scale Iterative Stereo Matching
Existing Vision Foundation Model (VFM)-based iterative stereo pipelines under-exploit three information pathways: multi-scale backbone features are collapsed into single-level correlations, geometric priors remain untapped at initialization, and context propagates only locally. These gaps widen under degraded photometric cues, making underwater scenes a stringent generalization test. To address this, we propose LinStereo, built upon Depth Anything V3, whose core is a Position-Aware Linear Attention (PALA) module that replaces local recurrence with global aggregation at linear cost, propagating reliable estimates from well-matched regions into degraded areas while preserving disparity stru...
| Authors | Yiran Wang, Oliver Turner, Viorela Ila |
|---|---|
| Primary category | cs.CV |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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