GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised
  Cross-View Localization
Authors: name: Shaowen Tong, name: Zimin Xia, name: Alexandre Alahi, name: Xuming He, name: Yujiao Shi

Cross-view localization, the task of estimating a camera's
3-degrees-of-freedom (3-DoF) pose by aligning ground-level images with
satellite images, is crucial for large-scale outdoor applications like
autonomous navigation and augmented reality. Existing methods often rely on
fully supervised learning, which requires costly ground-truth pose annotations.
In this work, we propose GeoDistill, a Geometry guided weakly supervised self
distillation framework that uses teacher-student learning with Field-of-View
(FoV)-based masking to enhance local feature learning for robust cross-view
localization. In GeoDistill, the teacher model localizes a panoramic image,
while the student model predicts locations from a limited FoV counterpart
created by FoV-based masking. By aligning the student's predictions with those
of the teacher, the student focuses on key features like lane lines and ignores
textureless regions, such as roads. This results in more accurate predictions
and reduced uncertainty, regardless of whether the query images are panoramas
or limited FoV images. Our experiments show that GeoDistill significantly
improves localization performance across different frameworks. Additionally, we
introduce a novel orientation estimation network that predicts relative
orientation without requiring precise planar position ground truth. GeoDistill
provides a scalable and efficient solution for real-world cross-view
localization challenges. Code and model can be found at
https://github.com/tongshw/GeoDistill.