# MegaLoc: One Retrieval to Place Them All

arXiv: https://arxiv.org/abs/2502.17237 · HTML: https://mlsanity.com/paper/2502.17237/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval](https://mlsanity.com/paper/2004.01804/) (2020)
- [Global Structure-from-Motion Revisited](https://mlsanity.com/paper/2407.20219/) (2024)
- [Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking](https://mlsanity.com/paper/1803.11285/) (2018)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [MixVPR: Feature Mixing for Visual Place Recognition](https://mlsanity.com/paper/2303.02190/) (2023)
- [Optimal Transport Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2311.15937/) (2023)
- [EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition](https://mlsanity.com/paper/2308.10832/) (2023)
- [LaMAR: Benchmarking Localization and Mapping for Augmented Reality](https://mlsanity.com/paper/2210.10770/) (2022)
- [GSV-Cities: Toward Appropriate Supervised Visual Place Recognition](https://mlsanity.com/paper/2210.10239/) (2022)
- [Deep Visual Geo-localization Benchmark](https://mlsanity.com/paper/2204.03444/) (2022)
- [CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition](https://mlsanity.com/paper/2402.19231/) (2024)
- [MegaScenes: Scene-Level View Synthesis at Scale](https://mlsanity.com/paper/2406.11819/) (2024)
- [Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition](https://mlsanity.com/paper/2407.02422/) (2024)
- [Are Local Features All You Need for Cross-Domain Visual Place Recognition?](https://mlsanity.com/paper/2304.05887/) (2023)

## Built on by (most cited first, 30 of 60)

- [Emergent Outlier View Rejection in Visual Geometry Grounded Transformers](https://mlsanity.com/paper/2512.04012/) (2025)
- [Scaling Image Geo-Localization to Continent Level](https://mlsanity.com/paper/2510.26795/) (2025)
- [Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset](https://mlsanity.com/paper/2506.04224/) (2025)
- [UniPR-3D: Towards Universal Visual Place Recognition with Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2512.21078/) (2025)
- [Global Structure-from-Motion Meets Feedforward Reconstruction](https://mlsanity.com/paper/2605.26103/) (2026)
- [Ensemble-Based Event Camera Place Recognition Under Varying Illumination](https://mlsanity.com/paper/2509.01968/) (2025)
- [Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments](https://mlsanity.com/paper/2503.04096/) (2025)
- [To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition](https://mlsanity.com/paper/2504.06116/) (2025)
- [Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition](https://mlsanity.com/paper/2604.03277/) (2026)
- [AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision](https://mlsanity.com/paper/2604.26567/) (2026)
- [SAGE: Spatial-visual Adaptive Graph Exploration for Efficient Visual Place Recognition](https://mlsanity.com/paper/2509.25723/) (2025)
- [UniMPR: A Unified Framework for Multimodal Place Recognition with Heterogeneous Sensor Configurations](https://mlsanity.com/paper/2512.18279/) (2025)
- [Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition](https://mlsanity.com/paper/2602.04401/) (2026)
- [TIGeR: A Unified Framework for Time, Images and Geo-location Retrieval](https://mlsanity.com/paper/2603.24749/) (2026)
- [MV-RoMa: From Pairwise Matching into Multi-View Track Reconstruction](https://mlsanity.com/paper/2603.27542/) (2026)
- [Region Matters: Efficient and Reliable Region-Aware Visual Place Recognition](https://mlsanity.com/paper/2604.22390/) (2026)
- [VidMap: Exploiting Temporal Structure for Video-Based Structure-from-Motion](https://mlsanity.com/paper/2607.27194/) (2026)
- [Query-Based Adaptive Aggregation for Multi-Dataset Joint Training Toward Universal Visual Place Recognition](https://mlsanity.com/paper/2507.03831/) (2025)
- [ImLoc: Revisiting Visual Localization with Image-based Representation](https://mlsanity.com/paper/2601.04185/) (2026)
- [Global-Aware Edge Prioritization for Pose Graph Initialization](https://mlsanity.com/paper/2602.21963/) (2026)
- [EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2603.05807/) (2026)
- [Diversity-aware View Partitioning for Scalable VGGT](https://mlsanity.com/paper/2607.01885/) (2026)
- [Reference-Induced Consensus for Selective Posed-Reference Visual Localization](https://mlsanity.com/paper/2607.04722/) (2026)
- [Hi^2-GSLoc: Dual-Hierarchical Gaussian-Specific Visual Relocalization for Remote Sensing](https://mlsanity.com/paper/2507.15683/) (2025)
- [TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing](https://mlsanity.com/paper/2510.04100/) (2025)
- [Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition](https://mlsanity.com/paper/2510.17739/) (2025)
- [Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone](https://mlsanity.com/paper/2512.02737/) (2025)
- [YOPO-Nav: Visual Navigation using 3DGS Graphs from One-Pass Videos](https://mlsanity.com/paper/2512.09903/) (2025)
- [Robust Scene Coordinate Regression via Geometrically-Consistent Global Descriptors](https://mlsanity.com/paper/2512.17226/) (2025)
- [MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding](https://mlsanity.com/paper/2512.17492/) (2025)

## Abstract (arXiv)

Retrieving images from the same location as a given query is an important component of multiple computer vision tasks, like Visual Place Recognition, Landmark Retrieval, Visual Localization, 3D reconstruction, and SLAM. However, existing solutions are built to specifically work for one of these tasks, and are known to fail when the requirements slightly change or when they meet out-of-distribution data. In this paper we combine a variety of existing methods, training techniques, and datasets to train a retrieval model, called MegaLoc, that is performant on multiple tasks. We find that MegaLoc (1) achieves state of the art on a large number of Visual Place Recognition datasets, (2) impressive results on common Landmark Retrieval datasets, and (3) sets a new state of the art for Visual Localization on the LaMAR datasets, where we only changed the retrieval method to the existing localization pipeline. The code for MegaLoc is available at https://github.com/gmberton/MegaLoc