Struct-Loc: Confidence-Aware Structural Localization Via Hierarchical Point Cloud Registration

Abstract

Localization systems often rely heavily on visual information, which can degrade under challenging conditions such as variable lighting, dynamic objects, or repetitive textures. To enhance robustness beyond single-image methods, we model localization as a structural point cloud registration problem, leveraging motion continuity and geometric consistency over time. This formulation reduces sensitivity to transient occlusions and appearance changes, enabling the system to resolve ambiguities that single-image techniques often cannot. In this work, we introduce Struct-Loc, a localization framework that advances structural point cloud registration through confidence-aware hierarchical localization. By estimating the reliability of structural regions and incorporating it into the matching process, Struct-Loc generates robust descriptors tailored for pose estimation. To achieve near real-time performance, Struct-Loc combines efficient point convolutional encoders, a caching mechanism, and a hierarchical coarse-to-fine matching strategy that progressively narrows the search space. It consistently outperforms strong baselines in both accuracy and runtime, while achieving a 100× compression of the global map compared to COLMAP, significantly improving storage efficiency. We validate Struct-Loc on the LaMAR benchmark, demonstrating its effectiveness and robustness under real-world conditions.