Wrivinder：迈向空间智能：将地面图像定位到卫星图像
Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery
AI总结：
Wrivinder通过聚合地面影像与卫星图像，实现零样本的几何驱动定位，提供首个全面的跨视角对齐基准。
摘要：
将地面级影像与已注册的卫星地图对齐对于制图、导航和态势感知至关重要，但在存在较大视角间隙或GPS不可靠时仍极具挑战性。我们介绍了Wrivinder，一种零样本、基于几何的框架，通过聚合多个地面照片来重建一致的3D场景，并将其对齐到卫星图像上。Wrivinder结合了SfM重建、3D高斯点散列、语义定位和单目深度基于的度量线索，以产生稳定的顶视图渲染，可以直接与卫星上下文匹配，实现精确的相机地理定位。为了支持对这一任务的系统评估，该任务缺乏合适的基准，我们还发布了MC-Sat，一个经过精心挑选的数据集，将多视角地面影像与已注册的卫星瓷砖链接起来，覆盖多样的户外环境。共同，Wrivinder和MC-Sat提供了一个全面的基础和测试平台，用于研究以几何为中心的跨视角对齐，而无需配对监督。在零样本实验中，Wrivinder在密集和大范围场景中实现了亚30米的定位精度，突显了基于几何的聚合在鲁棒的地面到卫星定位中的潜力。
英文摘要：
Aligning ground-level imagery with geo-registered satellite maps is crucial for mapping, navigation, and situational awareness, yet remains challenging under large viewpoint gaps or when GPS is unreliable. We introduce Wrivinder, a zero-shot, geometry-driven framework that aggregates multiple ground photographs to reconstruct a consistent 3D scene and align it with overhead satellite imagery. Wrivinder combines SfM reconstruction, 3D Gaussian Splatting, semantic grounding, and monocular depth--based metric cues to produce a stable zenith-view rendering that can be directly matched to satellite context for metrically accurate camera geo-localization. To support systematic evaluation of this task, which lacks suitable benchmarks, we also release MC-Sat, a curated dataset linking multi-view ground imagery with geo-registered satellite tiles across diverse outdoor environments. Together, Wrivinder and MC-Sat provide a first comprehensive baseline and testbed for studying geometry-centered cross-view alignment without paired supervision. In zero-shot experiments, Wrivinder achieves sub-30\,m geolocation accuracy across both dense and large-area scenes, highlighting the promise of geometry-based aggregation for robust ground-to-satellite localization.
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