LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images
This addresses the problem of accurate and robust visual localization for computer vision and robotics applications, representing an incremental improvement by integrating existing techniques like Gaussian Splatting into a novel pipeline.
The paper tackles visual localization by proposing LoGS, a pipeline that uses 3D Gaussian Splatting for scene representation, achieving state-of-the-art accuracy in camera pose estimation and robustness with fewer training images, as demonstrated on four large-scale datasets.
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.