Revisiting Cross-View Localization from Image Matching

• URL: http://arxiv.org/abs/2508.10716v1
• Date: Thu, 14 Aug 2025 14:57:31 GMT
• Title: Revisiting Cross-View Localization from Image Matching
• Authors: Panwang Xia, Qiong Wu, Lei Yu, Yi Liu, Mingtao Xiong, Lei Liang, Yongjun Zhang, Yi Wan,
• Abstract summary: Cross-view localization aims to estimate the 3 degrees of freedom pose of a ground-view image by registering it to aerial or satellite imagery.<n>Existing methods either regress poses directly or align features in a shared bird's-eye view (BEV) space.<n>We propose a novel framework that improves both matching and localization.
• Score: 12.411420734642988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-view localization aims to estimate the 3 degrees of freedom pose of a ground-view image by registering it to aerial or satellite imagery. It is essential in GNSS-denied environments such as urban canyons and disaster zones. Existing methods either regress poses directly or align features in a shared bird's-eye view (BEV) space, both built upon accurate spatial correspondences between perspectives. However, these methods fail to establish strict cross-view correspondences, yielding only coarse or geometrically inconsistent matches. Consequently, fine-grained image matching between ground and aerial views remains an unsolved problem, which in turn constrains the interpretability of localization results. In this paper, we revisit cross-view localization from the perspective of cross-view image matching and propose a novel framework that improves both matching and localization. Specifically, we introduce a Surface Model to model visible regions for accurate BEV projection, and a SimRefiner module to refine the similarity matrix through local-global residual correction, eliminating the reliance on post-processing like RANSAC. To further support research in this area, we introduce CVFM, the first benchmark with 32,509 cross-view image pairs annotated with pixel-level correspondences. Extensive experiments demonstrate that our approach substantially improves both localization accuracy and image matching quality, setting new baselines under extreme viewpoint disparity.

Related papers

• CLNet: Cross-View Correspondence Makes a Stronger Geo-Localizationer [48.52152634356309]
  We propose a correspondence-aware feature refinement framework, termed CLNet, that explicitly bridges the semantic and geometric gaps between different views.<n> CLNet decomposes the view alignment process into three learnable and complementary modules.<n>Our proposed CLNet achieves state-of-the-art performance while offering better interpretability and generalizability.
  arXiv  Detail & Related papers  (2025-12-16T16:31:41Z)
• Loc$^2$: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching [80.57282092735991]
  We propose an accurate and interpretable fine-grained cross-view localization method.<n>It estimates the 3 Degrees of Freedom (DoF) pose of a ground-level image by matching its local features with a reference aerial image.<n> Experiments show state-of-the-art accuracy in challenging scenarios such as cross-area testing and unknown orientation.
  arXiv  Detail & Related papers  (2025-09-11T18:52:16Z)
• FG$^2$: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching [69.81167130510333]
  We propose a novel fine-grained cross-view localization method that estimates the 3 Degrees of Freedom pose of a ground-level image in an aerial image of the surroundings.<n>The pose is estimated by aligning a point plane generated from the ground image with a point plane sampled from the aerial image.<n>Compared to the previous state-of-the-art, our method reduces the mean localization error by 28% on the VIGOR cross-area test set.
  arXiv  Detail & Related papers  (2025-03-24T14:34:20Z)
• BevSplat: Resolving Height Ambiguity via Feature-Based Gaussian Primitives for Weakly-Supervised Cross-View Localization [11.50186721264038]
  This paper addresses the problem of weakly supervised cross-view localization.<n>The goal is to estimate the pose of a ground camera relative to a satellite image with noisy ground truth annotations.<n>We propose BevSplat, a novel method that resolves height ambiguity by using feature-based Gaussian primitives.
  arXiv  Detail & Related papers  (2025-02-13T08:54:04Z)
• Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings [39.252555758596706]
  Cross-View Geo-Localization matches street-view query images with geo-tagged aerial-view reference images.<n>Decentrality is a critical factor warranting deeper investigation, as larger decentrality can substantially improve localization efficiency but comes at the cost of declines in localization accuracy.<n>We introduce DReSS, a novel dataset designed to evaluate cross-view geo-localization with a large geographic scope and diverse landscapes.
  arXiv  Detail & Related papers  (2024-12-16T08:07:53Z)
• Unsupervised Multi-view UAV Image Geo-localization via Iterative Rendering [31.716967688739036]
  Unmanned Aerial Vehicle (UAV) Cross-View Geo-Localization (CVGL) presents significant challenges. Existing methods rely on the supervision of labeled datasets to extract viewpoint-invariant features for cross-view retrieval. We propose an unsupervised solution that lifts the scene representation to 3d space from UAV observations for satellite image generation.
  arXiv  Detail & Related papers  (2024-11-22T09:22:39Z)
• Breaking the Frame: Visual Place Recognition by Overlap Prediction [53.17564423756082]
  We propose a novel visual place recognition approach based on overlap prediction, called VOP.<n>VOP proceeds co-visible image sections by obtaining patch-level embeddings using a Vision Transformer backbone.<n>Our approach uses a voting mechanism to assess overlap scores for potential database images.
  arXiv  Detail & Related papers  (2024-06-23T20:00:20Z)
• Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator [12.415973198004169]
  We introduce a novel approach to fine-grained cross-view geo-localization. Our method aligns a warped ground image with a corresponding GPS-tagged satellite image covering the same area. operating at a speed of 30 FPS, our method outperforms state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-08-31T17:59:24Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization [89.69214577915959]
  This paper tackles the problem of Cross-view Video-based camera localization. We propose estimating the query camera's relative displacement to a satellite image before similarity matching. Experiments have demonstrated the effectiveness of video-based localization over single image-based localization.
  arXiv  Detail & Related papers  (2022-08-07T07:35:17Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)
• Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching [95.64702426906466]
  Cross-view geo-localization is a problem given a large-scale database of geo-tagged aerial images. Knowing orientation between ground and aerial images can significantly reduce matching ambiguity between these two views. We design a Dynamic Similarity Matching network to estimate cross-view orientation alignment during localization.
  arXiv  Detail & Related papers  (2020-05-08T05:21:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.