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arXiv · 2607.27139

SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI

Álvaro Díaz-Laureano·Roger Marí·Elías Masquil·Pablo Arias·Gabriele Facciolo
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Abstract

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 matches the accuracy of state-of-the-art LiDAR-supervised models, while producing sharper geometric details without requiring aligned real multi-date training products or LiDAR-derived labels. As a result, SeasonStereo offers a practical path toward large-scale 3D reconstruction from heterogeneous satellite images with reduced supervision cost.

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Álvaro Díaz-Laureano, Roger Marí, Elías Masquil, Pablo Arias, Gabriele Facciolo. 2026-07-29. SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI. https://arxiv.org/abs/2607.27139

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