HGSLoc: 3DGS-based Heuristic Camera Pose Refinement

TL;DR

HGSLoc is a lightweight, plug-and-play framework that combines 3D reconstruction and heuristic refinement to improve camera pose accuracy in visual localization, outperforming neural network-based methods especially in noisy and challenging environments.

Contribution

The paper introduces HGSLoc, a novel visual localization method that integrates explicit 3D geometric maps with heuristic refinement for improved accuracy and robustness.

Findings

Higher localization accuracy than NeRF-based methods

Robust performance in noisy and challenging environments

Effective in multiple benchmark datasets

Abstract

Visual localization refers to the process of determining camera poses and orientation within a known scene representation. This task is often complicated by factors such as changes in illumination and variations in viewing angles. In this paper, we propose HGSLoc, a novel lightweight plug-and-play pose optimization framework, which integrates 3D reconstruction with a heuristic refinement strategy to achieve higher pose estimation accuracy. Specifically, we introduce an explicit geometric map for 3D representation and high-fidelity rendering, allowing the generation of high-quality synthesized views to support accurate visual localization. Our method demonstrates higher localization accuracy compared to NeRF-based neural rendering localization approaches. We introduce a heuristic refinement strategy, its efficient optimization capability can quickly locate the target node, while we set the step level optimization step to enhance the pose accuracy in the scenarios with small errors. With carefully designed heuristic functions, it offers efficient optimization capabilities, enabling rapid error reduction in rough localization estimations. Our method mitigates the dependence on complex neural network models while demonstrating improved robustness against noise and higher localization accuracy in challenging environments, as compared to neural network joint optimization strategies. The optimization framework proposed in this paper introduces novel approaches to visual localization by integrating the advantages of 3D reconstruction and the heuristic refinement strategy, which demonstrates strong performance across multiple benchmark datasets, including 7Scenes and Deep Blending dataset. The implementation of our method has been released at https://github.com/anchang699/HGSLoc.