HGSLoc: 3DGS-based Heuristic Camera Pose Refinement

2024-09-17

arXiv.org

AI Summary

To address poor robustness and low real-time performance in visual localization under varying illumination and viewpoints, this paper proposes a lightweight, plug-and-play camera pose optimization framework. Methodologically, it introduces the first integration of explicit 3D Gaussian Splatting (3DGS)-based geometric mapping with a multi-stage heuristic pose refinement mechanism—replacing end-to-end neural joint optimization with stepwise pose updates driven by synthetic view rendering and reprojection error minimization. The core contribution is an explicit geometry-driven co-design paradigm that synergizes geometric representation and optimization, significantly improving convergence accuracy for small errors and robustness to noise. Evaluated on the 7Scenes and DB benchmarks, our method achieves a 2.1× speedup in rendering over NeRF-based localization approaches and improves pose accuracy by 19.6%, while maintaining high-fidelity reconstruction and enabling real-time, robust localization.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

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 illumination changes 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 a faster rendering speed and 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 heuristic refinement strategy, which demonstrates strong performance across multiple benchmark datasets, including 7Scenes and DB dataset.

Problem

• Improves camera pose accuracy in visual localization
• Reduces dependency on complex neural network models
• Enhances robustness against noise and challenging environments

Innovation

• 3DGS-based heuristic camera pose refinement
• Lightweight plug-and-play pose optimization framework
• Explicit geometric map for high-fidelity rendering