# On the Estimation of Image-matching Uncertainty in Visual Place Recognition

arXiv: https://arxiv.org/abs/2404.00546 · HTML: https://mlsanity.com/paper/2404.00546/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 58 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 27 of 27)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [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)
- [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)
- [MixVPR: Feature Mixing for Visual Place Recognition](https://mlsanity.com/paper/2303.02190/) (2023)
- [Modelling Uncertainty in Deep Learning for Camera Relocalization](https://mlsanity.com/paper/1509.05909/) (2015)
- [TransVPR: Transformer-based place recognition with multi-level attention aggregation](https://mlsanity.com/paper/2201.02001/) (2022)
- [VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change](https://mlsanity.com/paper/2005.08135/) (2020)
- [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)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network](https://mlsanity.com/paper/1707.09733/) (2017)
- [Benchmarking Image Retrieval for Visual Localization](https://mlsanity.com/paper/2011.11946/) (2020)
- [Viewpoint Invariant Dense Matching for Visual Geolocalization](https://mlsanity.com/paper/2109.09827/) (2021)
- [CoordiNet: uncertainty-aware pose regressor for reliable vehicle localization](https://mlsanity.com/paper/2103.10796/) (2021)
- [Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval](https://mlsanity.com/paper/2011.12663/) (2020)
- [Generalized Contrastive Optimization of Siamese Networks for Place Recognition](https://mlsanity.com/paper/2103.06638/) (2021)
- [STUN: Self-Teaching Uncertainty Estimation for Place Recognition](https://mlsanity.com/paper/2203.01851/) (2022)
- [Geometrically Mappable Image Features](https://mlsanity.com/paper/2003.09682/) (2020)
- [Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network](https://mlsanity.com/paper/1904.03182/) (2019)
- [CoPR: Towards Accurate Visual Localization With Continuous Place-descriptor Regression](https://mlsanity.com/paper/2304.07426/) (2023)

## Built on by (most cited first, 16 of 16)

- [Scaling Image Geo-Localization to Continent Level](https://mlsanity.com/paper/2510.26795/) (2025)
- [GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection](https://mlsanity.com/paper/2407.11736/) (2024)
- [LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation](https://mlsanity.com/paper/2410.04419/) (2024)
- [To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition](https://mlsanity.com/paper/2504.06116/) (2025)
- [Through the Lens of Doubt: Robust and Efficient Uncertainty Estimation for Visual Place Recognition](https://mlsanity.com/paper/2510.13464/) (2025)
- [Range and Bird's Eye View Fused Cross-Modal Visual Place Recognition](https://mlsanity.com/paper/2502.11742/) (2025)
- [Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition](https://mlsanity.com/paper/2602.04401/) (2026)
- [Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation](https://mlsanity.com/paper/2506.15988/) (2025)
- [QdaVPR: A novel query-based domain-agnostic model for visual place recognition](https://mlsanity.com/paper/2603.07414/) (2026)
- [Learning Visual Information Utility with PIXER](https://mlsanity.com/paper/2409.13151/) (2024)
- [The Overlooked Value of Test-time Reference Sets in Visual Place Recognition](https://mlsanity.com/paper/2510.03751/) (2025)
- [Cognitive Fusion of ZC Sequences and Time-Frequency Images for Out-of-Distribution Detection of Drone Signals](https://mlsanity.com/paper/2601.18326/) (2026)
- [KappaPlace: Learning Hyperspherical Uncertainty for Visual Place Recognition via Prototype-Anchored Supervision](https://mlsanity.com/paper/2605.19435/) (2026)
- [Route-MHT: Multimodal Transformer Guardrails for Thermal Visual Place Recognition](https://mlsanity.com/paper/2607.04745/) (2026)
- [Breaking D'ej`a Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning](https://mlsanity.com/paper/2607.12818/) (2026)
- [Recovering from Pose-Selection Failures in Repeated Visual Localization](https://mlsanity.com/paper/2610.16409/) (2026)

## Abstract (arXiv)

In Visual Place Recognition (VPR) the pose of a query image is estimated by comparing the image to a map of reference images with known reference poses. As is typical for image retrieval problems, a feature extractor maps the query and reference images to a feature space, where a nearest neighbor search is then performed. However, till recently little attention has been given to quantifying the confidence that a retrieved reference image is a correct match. Highly certain but incorrect retrieval can lead to catastrophic failure of VPR-based localization pipelines. This work compares for the first time the main approaches for estimating the image-matching uncertainty, including the traditional retrieval-based uncertainty estimation, more recent data-driven aleatoric uncertainty estimation, and the compute-intensive geometric verification. We further formulate a simple baseline method, ``SUE'', which unlike the other methods considers the freely-available poses of the reference images in the map. Our experiments reveal that a simple L2-distance between the query and reference descriptors is already a better estimate of image-matching uncertainty than current data-driven approaches. SUE outperforms the other efficient uncertainty estimation methods, and its uncertainty estimates complement the computationally expensive geometric verification approach. Future works for uncertainty estimation in VPR should consider the baselines discussed in this work.