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.
视觉定位涉及估计查询图像的6自由度（6-DoF）相机姿态，这是各种计算机视觉和机器人任务中的核心组成部分。本文提出了LoGS，一个基于视觉的定位管线，利用3D高斯散射（GS）技术作为场景表示。该新颖的表示方法支持高质量的新视角合成。在建图阶段，首先应用结构从运动（SfM）方法，然后生成GS地图。在定位过程中，初始位置通过图像检索获得，并结合局部特征匹配和PnP求解器，接着通过在GS地图上的基于合成分析的方式获得高精度姿态。四个大规模数据集上的实验结果表明，该方法在相机姿态估计方面达到了当前最先进（SoTA）的精度，并在少样本挑战条件下表现出较强的鲁棒性。