# View Consistent Purification for Accurate Cross-View Localization arXiv: https://arxiv.org/abs/2308.08110 ยท HTML: https://mlsanity.com/paper/2308.08110/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 39 references that are in the map. - First posted on arXiv 16 Aug 2023. ## Builds on (most cited first, 23 of 23) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017) - [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016) - [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016) - [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021) - [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) - [SoftTriple Loss: Deep Metric Learning Without Triplet Sampling](https://mlsanity.com/paper/1909.05235/) (2019) - [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) - [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) - [Visual Cross-View Metric Localization with Dense Uncertainty Estimates](https://mlsanity.com/paper/2208.08519/) (2022) - [Ford Multi-AV Seasonal Dataset](https://mlsanity.com/paper/2003.07969/) (2020) - [LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization](https://mlsanity.com/paper/2010.06323/) (2020) - [Learning to Localize Using a LiDAR Intensity Map](https://mlsanity.com/paper/2012.10902/) (2020) - [Satellite Image Based Cross-view Localization for Autonomous Vehicle](https://mlsanity.com/paper/2207.13506/) (2022) - [RSL-Net: Localising in Satellite Images From a Radar on the Ground](https://mlsanity.com/paper/2001.03233/) (2020) - [Localization Requirements for Autonomous Vehicles](https://mlsanity.com/paper/1906.01061/) (2019) - [Any Way You Look At It: Semantic Crossview Localization and Mapping with LiDAR](https://mlsanity.com/paper/2203.08925/) (2022) - [Learning to Localize Through Compressed Binary Maps](https://mlsanity.com/paper/2012.10942/) (2020) - [Aerial Imagery based LIDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2003.11192/) (2020) - [Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors](https://mlsanity.com/paper/2203.03334/) (2022) ## Built on by (most cited first, 15 of 15) - [FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching](https://mlsanity.com/paper/2503.18725/) (2025) - [Statewide Visual Geolocalization in the Wild](https://mlsanity.com/paper/2409.16763/) (2024) - [BevSplat: Resolving Height Ambiguity via Feature-Based Gaussian Primitives for Weakly-Supervised Cross-View Localization](https://mlsanity.com/paper/2502.09080/) (2025) - [Loc2: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching](https://mlsanity.com/paper/2509.09792/) (2025) - [PIDLoc: Cross-View Pose Optimization Network Inspired by PID Controllers](https://mlsanity.com/paper/2503.02388/) (2025) - [Homography Guided Temporal Fusion for Road Line and Marking Segmentation](https://mlsanity.com/paper/2404.07626/) (2024) - [CVD-SfM: A Cross-View Deep Front-end Structure-from-Motion System for Sparse Localization in Multi-Altitude Scenes](https://mlsanity.com/paper/2508.01936/) (2025) - [Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation](https://mlsanity.com/paper/2603.22153/) (2026) - [RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization](https://mlsanity.com/paper/2603.27758/) (2026) - [VIRD: View-Invariant Representation through Dual-Axis Transformation for Cross-View Pose Estimation](https://mlsanity.com/paper/2603.12918/) (2026) - [CIPER: A Unified Framework for Cross-view Image-retrieval and Pose-estimation](https://mlsanity.com/paper/2606.05011/) (2026) - [Fusing Satellite Imagery and Planimetric Maps for Cross-View Localization](https://mlsanity.com/paper/2606.10166/) (2026) - [Cross-View Yaw Estimation in Location Uncertainty with Line-Aligning Yaw Scoring](https://mlsanity.com/paper/2606.22094/) (2026) - [Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving](https://mlsanity.com/paper/2608.10660/) (2026) - [OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization](https://mlsanity.com/paper/2608.25274/) (2026) ## Abstract (arXiv) This paper proposes a fine-grained self-localization method for outdoor robotics that utilizes a flexible number of onboard cameras and readily accessible satellite images. The proposed method addresses limitations in existing cross-view localization methods that struggle to handle noise sources such as moving objects and seasonal variations. It is the first sparse visual-only method that enhances perception in dynamic environments by detecting view-consistent key points and their corresponding deep features from ground and satellite views, while removing off-the-ground objects and establishing homography transformation between the two views. Moreover, the proposed method incorporates a spatial embedding approach that leverages camera intrinsic and extrinsic information to reduce the ambiguity of purely visual matching, leading to improved feature matching and overall pose estimation accuracy. The method exhibits strong generalization and is robust to environmental changes, requiring only geo-poses as ground truth. Extensive experiments on the KITTI and Ford Multi-AV Seasonal datasets demonstrate that our proposed method outperforms existing state-of-the-art methods, achieving median spatial accuracy errors below 0.5 meters along the lateral and longitudinal directions, and a median orientation accuracy error below 2 degrees.