# Perspective from a Broader Context: Can Room Style Knowledge Help Visual Floorplan Localization?

arXiv: https://arxiv.org/abs/2508.01216 · HTML: https://mlsanity.com/paper/2508.01216/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling](https://mlsanity.com/paper/1908.00222/) (2019)
- [LaMAR: Benchmarking Localization and Mapping for Augmented Reality](https://mlsanity.com/paper/2210.10770/) (2022)
- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [Memory-Augmented Reinforcement Learning for Image-Goal Navigation](https://mlsanity.com/paper/2101.05181/) (2021)
- [Particle Filter Networks with Application to Visual Localization](https://mlsanity.com/paper/1805.08975/) (2018)
- [Curious Representation Learning for Embodied Intelligence](https://mlsanity.com/paper/2105.01060/) (2021)
- [Learning Navigational Visual Representations with Semantic Map Supervision](https://mlsanity.com/paper/2307.12335/) (2023)
- [LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments](https://mlsanity.com/paper/2104.09169/) (2021)
- [SPA: 3D Spatial-Awareness Enables Effective Embodied Representation](https://mlsanity.com/paper/2410.08208/) (2024)
- [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)
- [FloNa: Floor Plan Guided Embodied Visual Navigation](https://mlsanity.com/paper/2412.18335/) (2024)
- [Supercharging Floorplan Localization with Semantic Rays](https://mlsanity.com/paper/2507.09291/) (2025)
- [SeDAR - Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?](https://mlsanity.com/paper/1709.01500/) (2017)
- [Self-Supervised Image Representation Learning with Geometric Set Consistency](https://mlsanity.com/paper/2203.15361/) (2022)
- [Perspective from a Higher Dimension: Can 3D Geometric Priors Help Visual Floorplan Localization?](https://mlsanity.com/paper/2507.18881/) (2025)

## Built on by (most cited first, 2 of 2)

- [DisCo-FLoc: Semantic-Free Floorplan Localization via SE(2)-Aware Contrastive Disambiguation](https://mlsanity.com/paper/2601.01822/) (2026)
- [From Uncertainty to Determinism: Coarse-to-Fine Visual Floorplan Localization without Ray Matching](https://mlsanity.com/paper/2607.26817/) (2026)

## Abstract (arXiv)

Since a building's floorplan remains consistent over time and is inherently robust to changes in visual appearance, visual Floorplan Localization (FLoc) has received increasing attention from researchers. However, as a compact and minimalist representation of the building's layout, floorplans contain many repetitive structures (e.g., hallways and corners), thus easily result in ambiguous localization. Existing methods either pin their hopes on matching 2D structural cues in floorplans or rely on 3D geometry-constrained visual pre-trainings, ignoring the richer contextual information provided by visual images. In this paper, we suggest using broader visual scene context to empower FLoc algorithms with scene layout priors to eliminate localization uncertainty. In particular, we propose an unsupervised learning technique with clustering constraints to pre-train a room discriminator on self-collected unlabeled room images. Such a discriminator can empirically extract the hidden room type of the observed image and distinguish it from other room types. By injecting the scene context information summarized by the discriminator into an FLoc algorithm, the room style knowledge is effectively exploited to guide definite visual FLoc. We conducted sufficient comparative studies on two standard visual Floc benchmarks. Our experiments show that our approach outperforms state-of-the-art methods and achieves significant improvements in robustness and accuracy.