Perspective from a Higher Dimension: Can 3D Geometric Priors Help Visual Floorplan Localization?
Bolei Chen, Jiaxu Kang, Haonan Yang, Ping Zhong, Jianxin Wang

TL;DR
This paper introduces a novel 3D geometric prior approach for visual floorplan localization, leveraging multi-view constraints and scene surface reconstruction, significantly improving accuracy over existing methods without extra annotations.
Contribution
It proposes a new method that injects 3D geometric priors into 2D floorplan localization using self-supervised contrastive learning, addressing visual changes and occlusions.
Findings
Outperforms state-of-the-art methods in localization accuracy
Effectively bridges modal gaps with 3D priors
No additional annotations needed for modeling
Abstract
Since a building's floorplans are easily accessible, consistent over time, and inherently robust to changes in visual appearance, self-localization within the floorplan has attracted researchers' interest. However, since floorplans are minimalist representations of a building's structure, modal and geometric differences between visual perceptions and floorplans pose challenges to this task. While existing methods cleverly utilize 2D geometric features and pose filters to achieve promising performance, they fail to address the localization errors caused by frequent visual changes and view occlusions due to variously shaped 3D objects. To tackle these issues, this paper views the 2D Floorplan Localization (FLoc) problem from a higher dimension by injecting 3D geometric priors into the visual FLoc algorithm. For the 3D geometric prior modeling, we first model geometrically aware view…
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Taxonomy
TopicsAdvanced Vision and Imaging · Robotics and Sensor-Based Localization · 3D Shape Modeling and Analysis
