LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images

• URL: http://arxiv.org/abs/2410.11505v1
• Date: Tue, 15 Oct 2024 11:17:18 GMT
• Title: LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images
• Authors: Yuzhou Cheng, Jianhao Jiao, Yue Wang, Dimitrios Kanoulas,
• Abstract summary: This paper presents a vision-based localization pipeline utilizing the 3D Splatting (GS) technique as scene representation. During the mapping phase, structure-from-motion (SfM) is applied first, followed by the generation of a GS map. High-precision pose is achieved through the analysis-by manner on the map.
• Score: 7.363332481155945
• License:
• 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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