用航空影像匹配街景图，实现全州级精准定位。
Statewide Visual Geolocalization in the Wild
- 将大区域划分为统一分辨率的地理单元，构建联合嵌入空间。
- 在马萨诸塞州，60.6%的街景图定位误差小于50米。
- 适合需要大规模视觉定位的应用场景。
本文提出一种方法，通过匹配街景照片与航空参考影像数据库，在州级范围内预测野外拍摄的街景图像的地理位置。将搜索区域划分为地理单元，训练模型将单元与对应街景图映射到联合嵌入空间，用于测试时的检索。模型利用多尺度航空影像提供周边场景信息。提出一种具有统一单元分辨率的搜索区域布局，支持大规模地理区域扩展。实验表明，该方法能将上传至Mapillary平台的60.6%非全景街景图定位到真实位置50米以内。源代码已公开于https://github.com/fferflo/statewide-visual-geolocalization。
原文摘要 · Abstract (English)
This work presents a method that is able to predict the geolocation of a street-view photo taken in the wild within a state-sized search region by matching against a database of aerial reference imagery. We partition the search region into geographical cells and train a model to map cells and corresponding photos into a joint embedding space that is used to perform retrieval at test time. The model utilizes aerial images for each cell at multiple levels-of-detail to provide sufficient information about the surrounding scene. We propose a novel layout of the search region with consistent cell resolutions that allows scaling to large geographical regions. Experiments demonstrate that the method successfully localizes 60.6% of all non-panoramic street-view photos uploaded to the crowd-sourcing platform Mapillary in the state of Massachusetts to within 50m of their ground-truth location. Source code is available at https://github.com/fferflo/statewide-visual-geolocalization.
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