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arXiv · 2410.11505

LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images

Yuzhou Cheng·Jianhao Jiao·Yue Wang·Dimitrios Kanoulas
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Abstract

Visual localization involves estimating a query image's 6-DoF (degrees of freedom) camera pose, which is a fundamental component in various computer vision and robotic tasks. This paper presents LoGS, a vision-based localization pipeline utilizing the 3D Gaussian Splatting (GS) technique as scene representation. This novel representation allows high-quality novel view synthesis. During the mapping phase, structure-from-motion (SfM) is applied first, followed by the generation of a GS map. During localization, the initial position is obtained through image retrieval, local feature matching coupled with a PnP solver, and then a high-precision pose is achieved through the analysis-by-synthesis manner on the GS map. Experimental results on four large-scale datasets demonstrate the proposed approach's SoTA accuracy in estimating camera poses and robustness under challenging few-shot conditions.

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Yuzhou Cheng, Jianhao Jiao, Yue Wang, Dimitrios Kanoulas. 2024-10-15. LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images. https://arxiv.org/abs/2410.11505

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