# Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization

arXiv: https://arxiv.org/abs/1809.01019 · HTML: https://mlsanity.com/paper/1809.01019/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

## Built on by (most cited first, 29 of 29)

- [Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors](https://mlsanity.com/paper/2103.17265/) (2021)
- [MeshLoc: Mesh-Based Visual Localization](https://mlsanity.com/paper/2207.10762/) (2022)
- [SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition](https://mlsanity.com/paper/2102.11603/) (2021)
- [Learning Camera Localization via Dense Scene Matching](https://mlsanity.com/paper/2103.16792/) (2021)
- [The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement](https://mlsanity.com/paper/2404.10438/) (2024)
- [Probabilistic Visual Place Recognition for Hierarchical Localization](https://mlsanity.com/paper/2105.03091/) (2021)
- [Visual Localization using Imperfect 3D Models from the Internet](https://mlsanity.com/paper/2304.05947/) (2023)
- [Continual Learning for Image-Based Camera Localization](https://mlsanity.com/paper/2108.09112/) (2021)
- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery](https://mlsanity.com/paper/2110.03912/) (2021)
- [Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2108.03344/) (2021)
- [A Guide to Structureless Visual Localization](https://mlsanity.com/paper/2504.17636/) (2025)
- [Improving Feature-based Visual Localization by Geometry-Aided Matching](https://mlsanity.com/paper/2211.08712/) (2022)
- [MeshVPR: Citywide Visual Place Recognition Using 3D Meshes](https://mlsanity.com/paper/2406.02776/) (2024)
- [RING#: PR-by-PE Global Localization with Roto-translation Equivariant Gram Learning](https://mlsanity.com/paper/2409.00206/) (2024)
- [LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images](https://mlsanity.com/paper/2410.11505/) (2024)
- [Yes, we CANN: Constrained Approximate Nearest Neighbors for local feature-based visual localization](https://mlsanity.com/paper/2306.09012/) (2023)
- [Combining Absolute and Semi-Generalized Relative Poses for Visual Localization](https://mlsanity.com/paper/2409.14269/) (2024)
- [A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition](https://mlsanity.com/paper/2412.06153/) (2024)
- [YOWO: You Only Walk Once to Jointly Map An Indoor Scene and Register Ceiling-mounted Cameras](https://mlsanity.com/paper/2511.16521/) (2025)
- [3D Surfel Map-Aided Visual Relocalization with Learned Descriptors](https://mlsanity.com/paper/2104.03856/) (2021)
- [SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition](https://mlsanity.com/paper/2106.11481/) (2021)
- [SuperGF: Unifying Local and Global Features for Visual Localization](https://mlsanity.com/paper/2212.13105/) (2022)
- [A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval](https://mlsanity.com/paper/2303.08398/) (2023)
- [Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization](https://mlsanity.com/paper/2306.17529/) (2023)
- [Scalable and Efficient Hierarchical Visual Topological Mapping](https://mlsanity.com/paper/2404.05023/) (2024)
- [Uncertainty-Aware Hierarchical Re-Localization in OpenStreetMap via Semantic Alignment](https://mlsanity.com/paper/2603.01613/) (2026)
- [AsymLoc: Towards Asymmetric Feature Matching for Efficient Visual Localization](https://mlsanity.com/paper/2604.09445/) (2026)
- [Privacy-Preserving Structureless Visual Localization via Image Obfuscation](https://mlsanity.com/paper/2604.12068/) (2026)

## Abstract (arXiv)

Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query image and 3D points in the model using local descriptors. However, computational power is often limited on robotic platforms, making this task challenging in large-scale environments. Binary feature descriptors significantly speed up this 2D-3D matching, and have become popular in the robotics community, but also strongly impair the robustness to perceptual aliasing and changes in viewpoint, illumination and scene structure. In this work, we propose to leverage recent advances in deep learning to perform an efficient hierarchical localization. We first localize at the map level using learned image-wide global descriptors, and subsequently estimate a precise pose from 2D-3D matches computed in the candidate places only. This restricts the local search and thus allows to efficiently exploit powerful non-binary descriptors usually dismissed on resource-constrained devices. Our approach results in state-of-the-art localization performance while running in real-time on a popular mobile platform, enabling new prospects for robotics research.