Reproduce “HGSLoc: 3DGS-based Heuristic Camera Pose Refinement” on OpenResearch

About this paper

Zhongyan Niu, Zhen Tan, Jinpu Zhang, Xueliang Yang, Dewen Hu

HGSLoc, developed by researchers at the National University of Defense Technology, introduces a framework leveraging 3D Gaussian Splatting and a heuristic refinement algorithm to enhance visual localization. It achieves improved camera pose accuracy and robustness, particularly when starting from noisy initial estimates, reducing median translation error on 7Scenes from 2.9cm to 2.1cm and significantly outperforming prior neural rendering localization methods.

Key insights

• The rapid, high-quality rendering capability of 3DGS is crucial for making an image-similarity-based heuristic search feasible in real-time.
• A dedicated heuristic search strategy can more effectively navigate the 6-DoF pose space and refine initial estimates than joint optimization or brute-force methods, especially when initial poses are noisy.
• The framework's plug-and-play design allows it to seamlessly integrate with various existing coarse pose estimators, universally enhancing their accuracy.

Summary by alphaXiv.