# CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization

arXiv: https://arxiv.org/abs/2208.03660 · HTML: https://mlsanity.com/paper/2208.03660/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 60 references that are in the map.
- First posted on arXiv 7 Aug 2022.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016)
- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization](https://mlsanity.com/paper/2204.00097/) (2022)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization](https://mlsanity.com/paper/2103.06818/) (2021)
- [Self-supervising Fine-grained Region Similarities for Large-scale Image Localization](https://mlsanity.com/paper/2006.03926/) (2020)
- [Bridging the Domain Gap for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/1904.11045/) (2019)
- [Localizing and Orienting Street Views Using Overhead Imagery](https://mlsanity.com/paper/1608.00161/) (2016)
- [Optimal Feature Transport for Cross-View Image Geo-Localization](https://mlsanity.com/paper/1907.05021/) (2019)
- [Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image](https://mlsanity.com/paper/2204.04752/) (2022)
- [Cross-View Image Matching for Geo-localization in Urban Environments](https://mlsanity.com/paper/1703.07815/) (2017)
- [LDSO: Direct Sparse Odometry with Loop Closure](https://mlsanity.com/paper/1808.01111/) (2018)
- [Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching](https://mlsanity.com/paper/2203.14148/) (2022)
- [Geometry-Guided Street-View Panorama Synthesis from Satellite Imagery](https://mlsanity.com/paper/2103.01623/) (2021)
- [Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation](https://mlsanity.com/paper/2005.11592/) (2020)
- [Visual-Inertial Monocular SLAM with Map Reuse](https://mlsanity.com/paper/1610.05949/) (2016)
- [Semantic Cross-View Matching](https://mlsanity.com/paper/1511.00098/) (2015)

## Built on by (most cited first, 25 of 25)

- [SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation](https://mlsanity.com/paper/2211.14651/) (2022)
- [Convolutional Cross-View Pose Estimation](https://mlsanity.com/paper/2303.05915/) (2023)
- [Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization](https://mlsanity.com/paper/2309.15556/) (2023)
- [Weakly-supervised Camera Localization by Ground-to-satellite Image Registration](https://mlsanity.com/paper/2409.06471/) (2024)
- [Adapting Fine-Grained Cross-View Localization to Areas without Fine Ground Truth](https://mlsanity.com/paper/2406.00474/) (2024)
- [Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models](https://mlsanity.com/paper/2408.02408/) (2024)
- [GAReT: Cross-view Video Geolocalization with Adapters and Auto-Regressive Transformers](https://mlsanity.com/paper/2408.02840/) (2024)
- [BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation](https://mlsanity.com/paper/2312.15363/) (2023)
- [Seeing through Satellite Images at Street Views](https://mlsanity.com/paper/2505.17001/) (2025)
- [BevSplat: Resolving Height Ambiguity via Feature-Based Gaussian Primitives for Weakly-Supervised Cross-View Localization](https://mlsanity.com/paper/2502.09080/) (2025)
- [EarthLoc: Astronaut Photography Localization by Indexing Earth from Space](https://mlsanity.com/paper/2403.06758/) (2024)
- [Cross-View Image Set Geo-Localization](https://mlsanity.com/paper/2412.18852/) (2024)
- [Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis](https://mlsanity.com/paper/2412.03315/) (2024)
- [BEVLoc: Cross-View Localization and Matching via Birds-Eye-View Synthesis](https://mlsanity.com/paper/2410.06410/) (2024)
- [Last-Meter Precision Navigation for UAVs: A Diffusion-Refined Aerial Visual Servoing Approach](https://mlsanity.com/paper/2607.04352/) (2026)
- [Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View Geo-Localization](https://mlsanity.com/paper/2502.11408/) (2025)
- [Temporal Attention for Cross-View Sequential Image Localization](https://mlsanity.com/paper/2408.15569/) (2024)
- [SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes](https://mlsanity.com/paper/2608.07835/) (2026)
- [Empowering Feed-Forward Reconstruction Models with Metric Scale via Satellite Images](https://mlsanity.com/paper/2606.08205/) (2026)
- [Cross-View Yaw Estimation in Location Uncertainty with Line-Aligning Yaw Scoring](https://mlsanity.com/paper/2606.22094/) (2026)
- [Trajectory-aware Cross-view Geo-localization with Sequential Observations](https://mlsanity.com/paper/2607.15491/) (2026)
- [Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving](https://mlsanity.com/paper/2608.10660/) (2026)
- [X2Localizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization](https://mlsanity.com/paper/2608.16658/) (2026)
- [ARC-Loc: Leveraging Azimuthal Ray Convergence as a Geometric Cue for Direct Cross-View Localization](https://mlsanity.com/paper/2609.04965/) (2026)
- [How to Reduce Localization Ambiguity? Geometry-Semantic Constrained BEV Representation Learning for Satellite-Ground Localization](https://mlsanity.com/paper/2609.39127/) (2026)

## Abstract (arXiv)

This paper tackles the problem of Cross-view Video-based camera Localization (CVL). The task is to localize a query camera by leveraging information from its past observations, i.e., a continuous sequence of images observed at previous time stamps, and matching them to a large overhead-view satellite image. The critical challenge of this task is to learn a powerful global feature descriptor for the sequential ground-view images while considering its domain alignment with reference satellite images. For this purpose, we introduce CVLNet, which first projects the sequential ground-view images into an overhead view by exploring the ground-and-overhead geometric correspondences and then leverages the photo consistency among the projected images to form a global representation. In this way, the cross-view domain differences are bridged. Since the reference satellite images are usually pre-cropped and regularly sampled, there is always a misalignment between the query camera location and its matching satellite image center. Motivated by this, we propose estimating the query camera's relative displacement to a satellite image before similarity matching. In this displacement estimation process, we also consider the uncertainty of the camera location. For example, a camera is unlikely to be on top of trees. To evaluate the performance of the proposed method, we collect satellite images from Google Map for the KITTI dataset and construct a new cross-view video-based localization benchmark dataset, KITTI-CVL. Extensive experiments have demonstrated the effectiveness of video-based localization over single image-based localization and the superiority of each proposed module over other alternatives.