# LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment

arXiv: https://arxiv.org/abs/2410.12269 · HTML: https://mlsanity.com/paper/2410.12269/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 76 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 30 of 39)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [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)
- [RoMa: Robust Dense Feature Matching](https://mlsanity.com/paper/2305.15404/) (2023)
- [Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC](https://mlsanity.com/paper/2002.12324/) (2020)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [MAGSAC: marginalizing sample consensus](https://mlsanity.com/paper/1803.07469/) (2018)
- [Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed](https://mlsanity.com/paper/2403.04765/) (2024)
- [Self-supervising Fine-grained Region Similarities for Large-scale Image Localization](https://mlsanity.com/paper/2006.03926/) (2020)
- [Hierarchical Scene Coordinate Classification and Regression for Visual Localization](https://mlsanity.com/paper/1909.06216/) (2019)
- [Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image](https://mlsanity.com/paper/2204.04752/) (2022)
- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [Detector-Free Structure from Motion](https://mlsanity.com/paper/2306.15669/) (2023)
- [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023)
- [MeshLoc: Mesh-Based Visual Localization](https://mlsanity.com/paper/2207.10762/) (2022)
- [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://mlsanity.com/paper/2212.07766/) (2022)
- [DeepI2P: Image-to-Point Cloud Registration via Deep Classification](https://mlsanity.com/paper/2104.03501/) (2021)
- [Is Geometry Enough for Matching in Visual Localization?](https://mlsanity.com/paper/2203.12979/) (2022)
- [Visual Cross-View Metric Localization with Dense Uncertainty Estimates](https://mlsanity.com/paper/2208.08519/) (2022)
- [Privacy Preserving Image-Based Localization](https://mlsanity.com/paper/1903.05572/) (2019)
- [SOLD2: Self-supervised Occlusion-aware Line Description and Detection](https://mlsanity.com/paper/2104.03362/) (2021)
- [Large-scale, real-time visual-inertial localization revisited](https://mlsanity.com/paper/1907.00338/) (2019)
- [Hybrid Scene Compression for Visual Localization](https://mlsanity.com/paper/1807.07512/) (2018)
- [SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding](https://mlsanity.com/paper/2306.05407/) (2023)
- [CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data](https://mlsanity.com/paper/2112.09081/) (2021)
- [S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching](https://mlsanity.com/paper/2004.01673/) (2020)
- [UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization](https://mlsanity.com/paper/2401.05971/) (2024)

## Built on by (most cited first, 13 of 13)

- [OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata](https://mlsanity.com/paper/2509.18350/) (2025)
- [LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment](https://mlsanity.com/paper/2507.00659/) (2025)
- [NTR-Gaussian: Nighttime Dynamic Thermal Reconstruction with 4D Gaussian Splatting Based on Thermodynamics](https://mlsanity.com/paper/2503.03115/) (2025)
- [Camera Pose Refinement via 3D Gaussian Splatting](https://mlsanity.com/paper/2508.17876/) (2025)
- [BuildAnyPoint: 3D Building Structured Abstraction from Diverse Point Clouds](https://mlsanity.com/paper/2602.23645/) (2026)
- [egenioussBench: A New Dataset for Geospatial Visual Localisation](https://mlsanity.com/paper/2605.05351/) (2026)
- [OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos](https://mlsanity.com/paper/2606.25245/) (2026)
- [SF-Recon: Simplification-Free Lightweight Building Reconstruction via 3D Gaussian Splatting](https://mlsanity.com/paper/2511.13278/) (2025)
- [Uncertainty-Aware Hierarchical Re-Localization in OpenStreetMap via Semantic Alignment](https://mlsanity.com/paper/2603.01613/) (2026)
- [LoD-Loc v3: Generalized Aerial Localization in Dense Cities using Instance Silhouette Alignment](https://mlsanity.com/paper/2603.19609/) (2026)
- [SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models](https://mlsanity.com/paper/2606.27444/) (2026)
- [PiLoT v2: Pixel-to-Orthogonal Map Alignment for Free-view UAV Geo-localization](https://mlsanity.com/paper/2606.31098/) (2026)
- [ScalLoc: Continuous Oblique Thermal UAV Geo-Localization with a 2D Satellite Map](https://mlsanity.com/paper/2610.14866/) (2026)

## Abstract (arXiv)

We propose a new method named LoD-Loc for visual localization in the air. Unlike existing localization algorithms, LoD-Loc does not rely on complex 3D representations and can estimate the pose of an Unmanned Aerial Vehicle (UAV) using a Level-of-Detail (LoD) 3D map. LoD-Loc mainly achieves this goal by aligning the wireframe derived from the LoD projected model with that predicted by the neural network. Specifically, given a coarse pose provided by the UAV sensor, LoD-Loc hierarchically builds a cost volume for uniformly sampled pose hypotheses to describe pose probability distribution and select a pose with maximum probability. Each cost within this volume measures the degree of line alignment between projected and predicted wireframes. LoD-Loc also devises a 6-DoF pose optimization algorithm to refine the previous result with a differentiable Gaussian-Newton method. As no public dataset exists for the studied problem, we collect two datasets with map levels of LoD3.0 and LoD2.0, along with real RGB queries and ground-truth pose annotations. We benchmark our method and demonstrate that LoD-Loc achieves excellent performance, even surpassing current state-of-the-art methods that use textured 3D models for localization. The code and dataset are available at https://victorzoo.github.io/LoD-Loc.github.io/.