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 this https URL.
Abstract (translated)
跨视角定位是指通过地面图像与卫星图像的对齐来估计相机的三自由度（3-DoF）姿态，这对于自动驾驶导航和增强现实等大规模户外应用至关重要。现有方法通常依赖于完全监督学习，这需要昂贵的地面真值姿势标注。在本研究中，我们提出了GeoDistill，这是一个基于几何引导弱监督自我蒸馏框架，利用教师模型与学生模型之间的教学，并通过视场（FoV）掩码增强局部特征的学习，从而提高跨视角定位的鲁棒性。 在GeoDistill中，教师模型对全景图像进行定位，而学生模型则预测由FoV基础掩码生成的有限视野对应图像的位置。通过将学生的预测与教师的预测对齐，学生会专注于关键特征（如车道线），并忽略无纹理区域（如道路）。这使得无论查询图像是全景还是有限视野图像，都能得到更准确的预测和减少不确定性。 我们的实验表明，GeoDistill在不同框架下显著提高了定位性能。此外，我们还引入了一种新颖的方向估计网络，该网络可以不依赖于精确平面位置的真实值来预测相对方向。GeoDistill为现实世界的跨视角定位挑战提供了一个可扩展且高效的解决方案。 代码和模型可以在提供的链接中找到：[https URL]（请将“https URL”替换为实际的网址）。
URL
https://arxiv.org/abs/2507.10935
