Reproduce “LSGS-Loc: Towards Robust 3DGS-Based Visual Localization for Large-Scale UAV Scenarios” on OpenResearch
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About this paper
Xiang Zhang, Tengfei Wang, Fang Xu, Xin Wang, Zongqian Zhan
LSGS-Loc presents a visual localization pipeline for large-scale UAV scenarios using 3D Gaussian Splatting, achieving centimeter-level precision by integrating a scale-aware pose initialization strategy and an artifact-mitigating photometric refinement mechanism. The method offers robust pose estimation in complex drone-view environments while being resilient to reconstruction artifacts.
Key insights
- Augmenting the reference database with synthetic views rendered from a 3DGS model significantly enhances retrieval robustness and initial pose quality for localization in challenging environments.
- A novel combination of relative pose estimation, a 3D scene-scale anchoring point, and a 1D photometric search effectively resolves scale ambiguity and provides a high-quality initial pose without scene-specific retraining.
- Leveraging Laplacian magnitudes to create a reliability mask for photometric loss computation during optimization directs pose updates towards geometrically consistent regions, improving robustness against 3DGS rendering artifacts.
Summary by alphaXiv.
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