# FocusTune: Tuning Visual Localization through Focus-Guided Sampling

arXiv: https://arxiv.org/abs/2311.02872 · HTML: https://mlsanity.com/paper/2311.02872/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 57 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 30 of 32)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [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)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016)
- [Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://mlsanity.com/paper/1704.00390/) (2017)
- [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017)
- [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021)
- [Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC](https://mlsanity.com/paper/2002.12324/) (2020)
- [Geometry-Aware Learning of Maps for Camera Localization](https://mlsanity.com/paper/1712.03342/) (2017)
- [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)
- [BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration](https://mlsanity.com/paper/1604.01093/) (2016)
- [Learning Multi-Scene Absolute Pose Regression with Transformers](https://mlsanity.com/paper/2103.11468/) (2021)
- [Accelerated Coordinate Encoding: Learning to Relocalize in Minutes using RGB and Poses](https://mlsanity.com/paper/2305.14059/) (2023)
- [Hierarchical Scene Coordinate Classification and Regression for Visual Localization](https://mlsanity.com/paper/1909.06216/) (2019)
- [Expert Sample Consensus Applied to Camera Re-Localization](https://mlsanity.com/paper/1908.02484/) (2019)
- [Robust Image Retrieval-based Visual Localization using Kapture](https://mlsanity.com/paper/2007.13867/) (2020)
- [On the Limits of Pseudo Ground Truth in Visual Camera Re-localisation](https://mlsanity.com/paper/2109.00524/) (2021)
- [Is Geometry Enough for Matching in Visual Localization?](https://mlsanity.com/paper/2203.12979/) (2022)
- [Hybrid Scene Compression for Visual Localization](https://mlsanity.com/paper/1807.07512/) (2018)
- [On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation](https://mlsanity.com/paper/1702.02779/) (2017)
- [Visual Localization via Few-Shot Scene Region Classification](https://mlsanity.com/paper/2208.06933/) (2022)
- [Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade](https://mlsanity.com/paper/1810.12163/) (2018)
- [Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System](https://mlsanity.com/paper/1809.05477/) (2018)
- [VS-Net: Voting with Segmentation for Visual Localization](https://mlsanity.com/paper/2105.10886/) (2021)

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

- [UAVScenes: A Multi-Modal Dataset for UAVs](https://mlsanity.com/paper/2507.22412/) (2025)
- [R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization](https://mlsanity.com/paper/2501.01421/) (2025)
- [LightLoc: Learning Outdoor LiDAR Localization at Light Speed](https://mlsanity.com/paper/2503.17814/) (2025)
- [ACE-G: Improving Generalization of Scene Coordinate Regression Through Query Pre-Training](https://mlsanity.com/paper/2510.11605/) (2025)
- [Representing 3D sparse map points and lines for camera relocalization](https://mlsanity.com/paper/2402.18011/) (2024)
- [Reprojection Errors as Prompts for Efficient Scene Coordinate Regression](https://mlsanity.com/paper/2409.04178/) (2024)
- [FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training](https://mlsanity.com/paper/2410.00702/) (2024)
- [Scene Coordinate Reconstruction Priors](https://mlsanity.com/paper/2510.12387/) (2025)
- [Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential Information](https://mlsanity.com/paper/2412.06488/) (2024)
- [FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization](https://mlsanity.com/paper/2408.12037/) (2024)
- [Leveraging Spatial Attention and Edge Context for Optimized Feature Selection in Visual Localization](https://mlsanity.com/paper/2410.12240/) (2024)
- [Improved 3D Point-Line Mapping Regression for Camera Relocalization](https://mlsanity.com/paper/2502.20814/) (2025)
- [A-SCoRe: Attention-based Scene Coordinate Regression for wide-ranging scenarios](https://mlsanity.com/paper/2503.13982/) (2025)
- [Robust Scene Coordinate Regression via Geometrically-Consistent Global Descriptors](https://mlsanity.com/paper/2512.17226/) (2025)
- [MultiLoc: Look Around As You Localize for Fast And Robust Visual Re-localization](https://mlsanity.com/paper/2603.27170/) (2026)
- [LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization](https://mlsanity.com/paper/2608.15317/) (2026)

## Abstract (arXiv)

We propose FocusTune, a focus-guided sampling technique to improve the performance of visual localization algorithms. FocusTune directs a scene coordinate regression model towards regions critical for 3D point triangulation by exploiting key geometric constraints. Specifically, rather than uniformly sampling points across the image for training the scene coordinate regression model, we instead re-project 3D scene coordinates onto the 2D image plane and sample within a local neighborhood of the re-projected points. While our proposed sampling strategy is generally applicable, we showcase FocusTune by integrating it with the recently introduced Accelerated Coordinate Encoding (ACE) model. Our results demonstrate that FocusTune both improves or matches state-of-the-art performance whilst keeping ACE's appealing low storage and compute requirements, for example reducing translation error from 25 to 19 and 17 to 15 cm for single and ensemble models, respectively, on the Cambridge Landmarks dataset. This combination of high performance and low compute and storage requirements is particularly promising for applications in areas like mobile robotics and augmented reality. We made our code available at urlhttps://github.com/sontung/focus-tune.