# X-View: Graph-Based Semantic Multi-View Localization

arXiv: https://arxiv.org/abs/1709.09905 · HTML: https://mlsanity.com/paper/1709.09905/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

## Built on by (most cited first, 30 of 43)

- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [Revisit Anything: Visual Place Recognition via Image Segment Retrieval](https://mlsanity.com/paper/2409.18049/) (2024)
- [A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition](https://mlsanity.com/paper/2107.02440/) (2021)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition](https://mlsanity.com/paper/2212.00937/) (2022)
- [U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization](https://mlsanity.com/paper/2310.13766/) (2023)
- [PlaceNav: Topological Navigation through Place Recognition](https://mlsanity.com/paper/2309.17260/) (2023)
- [CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition](https://mlsanity.com/paper/2303.17778/) (2023)
- [SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks](https://mlsanity.com/paper/2403.19474/) (2024)
- [SG-Reg: Generalizable and Efficient Scene Graph Registration](https://mlsanity.com/paper/2504.14440/) (2025)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [GOReloc: Graph-based Object-Level Relocalization for Visual SLAM](https://mlsanity.com/paper/2408.07917/) (2024)
- [Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs](https://mlsanity.com/paper/2506.07454/) (2025)
- [Unified Representation of Geometric Primitives for Graph-SLAM Optimization Using Decomposed Quadrics](https://mlsanity.com/paper/2108.08957/) (2021)
- [External Camera-based Mobile Robot Pose Estimation for Collaborative Perception with Smart Edge Sensors](https://mlsanity.com/paper/2303.03797/) (2023)
- [VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2409.19293/) (2024)
- [CLIP-Clique: Graph-based Correspondence Matching Augmented by Vision Language Models for Object-based Global Localization](https://mlsanity.com/paper/2410.03054/) (2024)
- [StructVPR++: Distill Structural and Semantic Knowledge with Weighting Samples for Visual Place Recognition](https://mlsanity.com/paper/2503.06601/) (2025)
- [VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations](https://mlsanity.com/paper/2603.16506/) (2026)
- [Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching](https://mlsanity.com/paper/2201.05977/) (2022)
- [AirLoc: Object-based Indoor Relocalization](https://mlsanity.com/paper/2304.00954/) (2023)
- [Automatic Vocabulary and Graph Verification for Accurate Loop Closure Detection](https://mlsanity.com/paper/2107.14611/) (2021)
- [Open-World Distributed Robot Self-Localization with Transferable Visual Vocabulary and Both Absolute and Relative Features](https://mlsanity.com/paper/2109.04569/) (2021)
- [Superquadric Object Representation for Optimization-based Semantic SLAM](https://mlsanity.com/paper/2109.09627/) (2021)
- [Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition](https://mlsanity.com/paper/2201.03212/) (2022)
- [SRVIO: Super Robust Visual Inertial Odometry for dynamic environments and challenging Loop-closure conditions](https://mlsanity.com/paper/2201.05386/) (2022)
- [A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition](https://mlsanity.com/paper/2202.03677/) (2022)

## Abstract (arXiv)

Global registration of multi-view robot data is a challenging task. Appearance-based global localization approaches often fail under drastic view-point changes, as representations have limited view-point invariance. This work is based on the idea that human-made environments contain rich semantics which can be used to disambiguate global localization. Here, we present X-View, a Multi-View Semantic Global Localization system. X-View leverages semantic graph descriptor matching for global localization, enabling localization under drastically different view-points. While the approach is general in terms of the semantic input data, we present and evaluate an implementation on visual data. We demonstrate the system in experiments on the publicly available SYNTHIA dataset, on a realistic urban dataset recorded with a simulator, and on real-world StreetView data. Our findings show that X-View is able to globally localize aerial-to-ground, and ground-to-ground robot data of drastically different view-points. Our approach achieves an accuracy of up to 85 % on global localizations in the multi-view case, while the benchmarked baseline appearance-based methods reach up to 75 %.