DisCo-FLoc: Semantic-Free Floorplan Localization via $SE(2)$-Aware Contrastive Disambiguation
Ping Zhong, Shiyong Meng, Bolei Chen, Tao Zou, Chaoxu Mu, Jianxin Wang

TL;DR
DisCo-FLoc introduces a semantic-free, contrastive learning approach for visual floorplan localization that effectively disambiguates poses by leveraging $SE(2)$-aware perturbations and a depth-aware geometric predictor.
Contribution
The paper presents a novel semantic-free method combining a depth-aware ray regression predictor and $SE(2)$-perturbed contrastive learning for improved localization accuracy.
Findings
DisCo-FLoc outperforms state-of-the-art semantic-based methods on benchmark datasets.
The method significantly reduces ambiguity in pose estimation caused by repetitive layouts.
It narrows the gap between positional and directional localization accuracy.
Abstract
Visual Floorplan Localization (FLoc) struggles with severe structural aliasing caused by repetitive minimalist layouts. This occurs because physically distant poses share highly similar visual-geometric features, which degrades spatial separability and angular discriminability. While existing methods attempt to mitigate these ambiguities by relying on costly semantic annotations, the resulting performance gains remain inherently limited. To address the above issues, we propose DisCo-FLoc, a semantic-free method for visual-geometric Contrastive Disambiguation. First, we introduce a depth-aware Ray Regression Predictor (RRP) that serves as a dense-to-ray geometric projector. By explicitly suppressing visual clutter along the vertical dimension, RRP projects monocular RGB images into 2D ray primitives, which are matched with floorplans to produce geometry-aware FLoc candidates. Second, to…
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