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.
@article{arxiv.2410.11505,
title = {LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images},
author = {Yuzhou Cheng and Jianhao Jiao and Yue Wang and Dimitrios Kanoulas},
journal= {arXiv preprint arXiv:2410.11505},
year = {2024}
}