用生成式AI解决卫星影像跨季节立体匹配难题
SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI
- 用合成图像训练模型，控制季节变化影响
- 无需真实配准影像或激光测高标签，精度达顶尖水平
- 适合大范围、低成本3D重建场景
从卫星影像中实现精准三维重建通常依赖近似同时获取的立体像对，限制了其在多时相场景下的应用，因不同日期影像存在季节与光照差异。训练对表观变化鲁棒的稠密立体匹配模型是长期挑战，因需大量对齐的多时相影像和真实几何标注，成本高昂。我们提出SeasonStereo，一种可扩展框架，通过在具有受控季节变化的合成图像对上训练，并利用基础模型提供的零样本几何先验，实现跨时相卫星影像的视差估计。SeasonStereo在精度上达到最先进的激光雷达监督模型水平，且生成更清晰的几何细节，无需对齐的真实多时相训练数据或激光测高标签。该方法为异质卫星影像的大规模三维重建提供了低监督成本的实用路径。
原文摘要 · Abstract (English)
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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