Combining Absolute and Semi-Generalized Relative Poses for Visual Localization

TL;DR

This paper proposes a method that combines structure-based and structure-less visual localization strategies, improving camera pose estimation accuracy especially when scene models are incomplete or unavailable.

Contribution

It introduces a novel approach to combine and select between 2D-3D and 2D-2D matching strategies for enhanced visual localization performance.

Findings

Combining both strategies improves localization accuracy.

The method is effective in scenarios with limited scene data.

It outperforms existing approaches in practical tests.

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.