# Supercharging Floorplan Localization with Semantic Rays

arXiv: https://arxiv.org/abs/2507.09291 · HTML: https://mlsanity.com/paper/2507.09291/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 12 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [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)
- [Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling](https://mlsanity.com/paper/1908.00222/) (2019)
- [DSAC - Differentiable RANSAC for Camera Localization](https://mlsanity.com/paper/1611.05705/) (2016)
- [Image-based localization using LSTMs for structured feature correlation](https://mlsanity.com/paper/1611.07890/) (2016)
- [Particle Filter Networks with Application to Visual Localization](https://mlsanity.com/paper/1805.08975/) (2018)
- [LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments](https://mlsanity.com/paper/2104.09169/) (2021)
- [Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network](https://mlsanity.com/paper/1903.01804/) (2019)
- [LASER: LAtent SpacE Rendering for 2D Visual Localization](https://mlsanity.com/paper/2204.00157/) (2022)
- [F3Loc: Fusion and Filtering for Floorplan Localization](https://mlsanity.com/paper/2403.03370/) (2024)
- [Fully Geometric Panoramic Localization](https://mlsanity.com/paper/2403.19904/) (2024)
- [Spatio-Temporal Graph Localization Networks for Image-based Navigation](https://mlsanity.com/paper/2204.13237/) (2022)
- [SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments](https://mlsanity.com/paper/2404.10527/) (2024)
- [Sparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots](https://mlsanity.com/paper/2203.15272/) (2022)

## Built on by (most cited first, 11 of 11)

- [Perspective from a Broader Context: Can Room Style Knowledge Help Visual Floorplan Localization?](https://mlsanity.com/paper/2508.01216/) (2025)
- [DisCo-FLoc: Semantic-Free Floorplan Localization via SE(2)-Aware Contrastive Disambiguation](https://mlsanity.com/paper/2601.01822/) (2026)
- [SceneAligner: 3D-Grounded Floorplan Localization in the Wild](https://mlsanity.com/paper/2605.22581/) (2026)
- [Z-FLoc: Zero-Shot Floorplan Localization via Geometric Primitives](https://mlsanity.com/paper/2606.04788/) (2026)
- [PlanaReLoc: Camera Relocalization in 3D Planar Primitives via Region-Based Structure Matching](https://mlsanity.com/paper/2603.20818/) (2026)
- [From Uncertainty to Determinism: Coarse-to-Fine Visual Floorplan Localization without Ray Matching](https://mlsanity.com/paper/2607.26817/) (2026)
- [AutoCompass: Accurate Visual Localization on Public Maps by Learning from Weak Labels](https://mlsanity.com/paper/2609.02798/) (2026)
- [GALoc: Gravity Aligned Wireframes for Depth-Free Monocular Floorplan Localization](https://mlsanity.com/paper/2609.08385/) (2026)
- [Multimodal Floorplan Encoding: Learning Dense Modality-Invariant Representations](https://mlsanity.com/paper/2609.12723/) (2026)
- [SlugTrails: An Egocentric Benchmark for Floor Plan Localization in Large Buildings](https://mlsanity.com/paper/2609.19876/) (2026)
- [FreeLoc: Online Floorplan Localization via Diffusion-Aided Pose Refinement](https://mlsanity.com/paper/2610.05011/) (2026)

## Abstract (arXiv)

Floorplans provide a compact representation of the building's structure, revealing not only layout information but also detailed semantics such as the locations of windows and doors. However, contemporary floorplan localization techniques mostly focus on matching depth-based structural cues, ignoring the rich semantics communicated within floorplans. In this work, we introduce a semantic-aware localization framework that jointly estimates depth and semantic rays, consolidating over both for predicting a structural-semantic probability volume. Our probability volume is constructed in a coarse-to-fine manner: We first sample a small set of rays to obtain an initial low-resolution probability volume. We then refine these probabilities by performing a denser sampling only in high-probability regions and process the refined values for predicting a 2D location and orientation angle. We conduct an evaluation on two standard floorplan localization benchmarks. Our experiments demonstrate that our approach substantially outperforms state-of-the-art methods, achieving significant improvements in recall metrics compared to prior works. Moreover, we show that our framework can easily incorporate additional metadata such as room labels, enabling additional gains in both accuracy and efficiency.