# Multiview Image-Based Localization

arXiv: https://arxiv.org/abs/2503.23577 · HTML: https://mlsanity.com/paper/2503.23577/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [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)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021)
- [Understanding the Limitations of CNN-based Absolute Camera Pose Regression](https://mlsanity.com/paper/1903.07504/) (2019)
- [Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC](https://mlsanity.com/paper/2002.12324/) (2020)
- [Learning Less is More - 6D Camera Localization via 3D Surface Regression](https://mlsanity.com/paper/1711.10228/) (2017)
- [Image-based localization using LSTMs for structured feature correlation](https://mlsanity.com/paper/1611.07890/) (2016)
- [Map-free Visual Relocalization: Metric Pose Relative to a Single Image](https://mlsanity.com/paper/2210.05494/) (2022)
- [Expert Sample Consensus Applied to Camera Re-Localization](https://mlsanity.com/paper/1908.02484/) (2019)
- [Revealing Scenes by Inverting Structure from Motion Reconstructions](https://mlsanity.com/paper/1904.03303/) (2019)
- [To Learn or Not to Learn: Visual Localization from Essential Matrices](https://mlsanity.com/paper/1908.01293/) (2019)
- [Privacy Preserving Image-Based Localization](https://mlsanity.com/paper/1903.05572/) (2019)
- [KFNet: Learning Temporal Camera Relocalization using Kalman Filtering](https://mlsanity.com/paper/2003.10629/) (2020)
- [Learning Camera Localization via Dense Scene Matching](https://mlsanity.com/paper/2103.16792/) (2021)
- [Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark](https://mlsanity.com/paper/2205.15761/) (2022)
- [DistillPose: Lightweight Camera Localization Using Auxiliary Learning](https://mlsanity.com/paper/2108.03819/) (2021)
- [Improving Feature-based Visual Localization by Geometry-Aided Matching](https://mlsanity.com/paper/2211.08712/) (2022)
- [Retrieval and Localization with Observation Constraints](https://mlsanity.com/paper/2108.08516/) (2021)
- [Benchmarking Pedestrian Odometry: The Brown Pedestrian Odometry Dataset (BPOD)](https://mlsanity.com/paper/2112.13018/) (2021)

## Abstract (arXiv)

The image retrieval (IR) approach to image localization has distinct advantages to the 3D and the deep learning (DNN) approaches: it is seen-agnostic, simpler to implement and use, has no privacy issues, and is computationally efficient. The main drawback of this approach is relatively poor localization in both position and orientation of the query camera when compared to the competing approaches. This paper represents a hybrid approach that stores only image features in the database like some IR methods, but relies on a latent 3D reconstruction, like 3D methods but without retaining a 3D scene reconstruction. The approach is based on two ideas: em (i) a novel proposal where query camera center estimation relies only on relative translation estimates but not relative rotation estimates through a decoupling of the two, and em (ii) a shift from computing optimal pose from estimated relative pose to computing optimal pose from multiview correspondences, thus cutting out the ``middle-man''. Our approach shows improved performance on the 7-Scenes and Cambridge Landmarks datasets while also improving on timing and memory footprint as compared to state-of-the-art.