mlsanityData to 14 Oct 2026

LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images

arXiv 2410.11505 ↗First posted 15 Oct 2024cs.CV cs.ROcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 37 of its 46 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

37 of 46 references.

References · 37

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25 more
  1. 2016DSAC - Differentiable RANSAC for Camera Localizationcited by 266
  2. 2017Geometric Loss Functions for Camera Pose Regression with Deep Learningcited by 224
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  22. 20246DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Modelcited by 28
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  25. 2020Curved patch mapping and tracking for irregular terrain modeling: Application to bipedal robot foot placementcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.