基于延时偏振影像的全球水下地理定位
Global Underwater Geolocation from Time-Lapse Polarization Imagery
利用偏振图像中天空偏振模式反演太阳高度角曲线，再用两阶段 Transformer 做全球地理定位；其物理引导合成管线（从少量站点扩增到 280 万条太阳高度角匹配序列）为视觉定位中的跨站点泛化提供了一条可复用的数据增广思路。
原文依据 A compact two-stage transformer reconstructs the solar-elevation curve and predicts geolocation.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论水下智能体要确定自身位置极其困难。卫星信号在水面以下几厘米内便消失；声学基线需要沉重的基础设施，且只能覆盖小范围区域。水下可见的天空偏振揭示了太阳的高度角。对于拥有时钟的智能体而言，一天内高度角的变化模式即可揭示其所在位置。然而，从偏振图像中恢复高度角非常困难。最优的地理定位方法在已见过数据的地点定位效果良好，但当数据来自新地点时精度会急剧下降。我们的物理引导合成管线将少量站点的大量偏振图像库扩展为 280 万条太阳高度角匹配的训练序列，覆盖不同纬度、季节和水体类型。一个紧凑的两阶段 Transformer 重建太阳高度角曲线并预测地理位置。在留一站点测试中，各站点平均的中位测地误差为 500 公里——相较先前的深度学习基线约提升八倍；在目标站点数据有限的情况下，中位误差可缩小至个位数公里。
英文原文摘要 1163 字符
It is extremely hard for an underwater agent to know where it is. Satellite signals disappear within centimeters of the surface; acoustic baselines require heavy infrastructure to instrument small regions. The polarization of the sky, visible underwater, reveals the elevation of the sun. The pattern of elevation over the day reveals location to an agent with a clock. However, recovering elevation from polarization images is very difficult. State-of-the-art (SOTA) geolocalization methods can localize well for locations where they have seen data, but accuracy collapses when the data comes from a new location. Our physics-guided synthesis pipeline expands a huge library of polarization images from a small set of sites to 2.8 million solar-elevation-matched training sequences spanning latitudes, seasons, and water types. A compact two-stage transformer reconstructs the solar-elevation curve and predicts geolocation. Under leave-one-site-out tests, the site averaged median geodesic error is 500km--about an eightfold improvement over previous deep-learning baselines; with limited target-site data, the median error contracts to single-digit kilometers.