GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization

TL;DR

GeoDistill introduces a geometry-guided self-distillation framework that enhances weakly supervised cross-view localization by focusing on key features and reducing reliance on costly annotations, improving accuracy across various scenarios.

Contribution

The paper proposes GeoDistill, a novel weakly supervised self-distillation method utilizing Field-of-View masking and a teacher-student model for improved cross-view localization.

Findings

Significant performance improvements across multiple frameworks.

Effective localization with limited or panoramic images.

Enhanced orientation estimation without precise position ground truth.

Abstract

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.