| title | SceneAligner: 3D-Grounded Floorplan Localization in the Wild |
|---|---|
| authors | Junhyeong Cho, Ruojin Cai, Hadar Averbuch-Elor |
| date | 2026-05-21 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.22581 |
| affiliation | Cornell University |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Many public buildings provide floorplans with a "you are here" indicator to help visitors orient themselves. Floorplan localization seeks to computationally replicate this capability by determining where visual observations were captured within a floorplan. However, existing methods typically assume controlled small-scale environments and precise vectorized floorplans, limiting their ability to operate in large-scale buildings and rasterized floorplans. In this work, we present an approach for performing floorplan localization in the wild by grounding the task in a reconstructed 3D representation of the scene. Given an unconstrained image collection, our method reconstructs a gravity-aligned 3D scene and projects it into a 2D density map that serves as a floorplan proxy. Floorplan localization is then formulated as aligning this proxy with the input floorplan via a 2D similarity transform. To bridge the appearance gap between density maps and architectural floorplans, we adapt a 2D foundation model to learn cross-modal correspondences, introducing a fine-tuning scheme that encourages semantically aligned matches while preserving structural consistency. Extensive experiments demonstrate substantial improvements over prior methods, including in extremely sparse settings with as little as a single input image. Our code and data will be publicly available. |
cite
@misc{cho2026scenealigner,
title={SceneAligner: 3D-Grounded Floorplan Localization in the Wild},
author={Junhyeong Cho and Ruojin Cai and Hadar Averbuch-Elor},
year={2026},
eprint={2605.22581},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.22581}
}