Combining Absolute and Semi-Generalized Relative Poses for Visual Localization

• URL: http://arxiv.org/abs/2409.14269v1
• Date: Sat, 21 Sep 2024 23:55:42 GMT
• Title: Combining Absolute and Semi-Generalized Relative Poses for Visual Localization
• Authors: Vojtech Panek, Torsten Sattler, Zuzana Kukelova,
• Abstract summary: State-of-the-art localization approaches use 2D-3D matches between pixels in a query image and 3D points in the scene for pose estimation. In contrast, structure-less methods rely on 2D-2D matches and do not require any 3D scene model. We show that combining both strategies improves localization performance in multiple practically relevant scenarios.
• Score: 39.2464667533733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual localization is the problem of estimating the camera pose of a given query image within a known scene. Most state-of-the-art localization approaches follow the structure-based paradigm and use 2D-3D matches between pixels in a query image and 3D points in the scene for pose estimation. These approaches assume an accurate 3D model of the scene, which might not always be available, especially if only a few images are available to compute the scene representation. In contrast, structure-less methods rely on 2D-2D matches and do not require any 3D scene model. However, they are also less accurate than structure-based methods. Although one prior work proposed to combine structure-based and structure-less pose estimation strategies, its practical relevance has not been shown. We analyze combining structure-based and structure-less strategies while exploring how to select between poses obtained from 2D-2D and 2D-3D matches, respectively. We show that combining both strategies improves localization performance in multiple practically relevant scenarios.

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