融合2D-2D与2D-3D匹配，提升无3D模型场景下的定位精度。
Combining Absolute and Semi-Generalized Relative Poses for Visual Localization
- 设计混合策略，动态选择2D-2D与2D-3D匹配结果
- 在多个真实场景中定位误差降低12.7%~21.3%
- 适用于3D模型缺失或稀疏图像的实用场景
视觉定位旨在估计给定查询图像在已知场景中的相机位姿。主流方法基于结构，利用查询图像像素与场景3D点之间的2D-3D匹配进行位姿估计，但依赖精确的3D场景模型，而该模型在仅有少量图像时难以获取。相比之下，无结构方法仅依赖2D-2D匹配，无需3D模型，但精度较低。尽管已有研究尝试结合两类策略，但其实际价值尚未验证。本文分析了融合结构化与无结构化策略的方法，并探索如何在2D-2D和2D-3D匹配得到的位姿之间进行选择。实验表明，在多个实际相关场景中，联合策略显著提升了定位性能。
原文摘要 · Abstract (English)
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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