SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI
Accurate 3D reconstruction from satellite imagery typically relies on near-simultaneous stereo pairs, limiting its applicability to diachronic settings where multi-date images exhibit varying seasonal and illumination conditions. Training dense stereo matching models robust to appearance changes is a long-standing challenge, as aligned multi-date imagery and ground-truth geometry are costly to obtain at scale. We propose SeasonStereo, a scalable framework that addresses disparity estimation from diachronic satellite images by training on synthetic image pairs with controlled seasonal appearance variation, while leveraging zero-shot geometric priors from foundation models. SeasonStereo mat...
| Authors | Álvaro Díaz-Laureano, Roger Marí, Elías Masquil et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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- Alvaritox/seasonstereo-dataDataset
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