GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised
  Cross-View Localization
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.
687eef1033947f780d9b4a6a
Shaowen Tong
claimed_verified
2025-07-22T07:49:20.834Z
674724c1f08f7cc178292259
/avatars/ac6beddb111dc9a4a6893647ddb9e627.svg
tsw
user
tsw200027
687eef1033947f780d9b4a6b
Zimin Xia
admin_assigned
2025-07-22T14:27:22.388Z
68190825435e30c0ceaa4f52
/avatars/ceaf9507d07f3640bb8ce3a8bd4b2f83.svg
Zimin Xia
user
xzm0221
687eef1033947f780d9b4a6c
Alexandre Alahi
admin_assigned
2025-07-22T14:27:30.527Z
682081c677669579f41fe4bf
/avatars/695da815e81712efd93cf3869764cfff.svg
alexandre alahi
user
aalahi
687eef1033947f780d9b4a6d
Xuming He
687eef1033947f780d9b4a6e
Yujiao Shi
GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised
  Cross-View Localization
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.
GeoDistill uses a geometry-guided weakly supervised self-distillation framework to improve cross-view localization by focusing on key features and reducing uncertainty.
cross-view localization 3-DoF pose fully supervised learning weakly supervised learning self-distillation teacher-student learning Field-of-View (FoV)-based masking local feature learning panoramic image limited FoV lane lines textureless regions orientation estimation network relative orientation