IHF-ReLoc: An Instance-Aware Hierarchical Framework for 2D-3D Visual Relocalization

Existing visual relocalization methods typically follow a coarse-to-fine pipeline, establishing patch-level correspondences between a query image and a pre-built 3D map before refining them into point-to-pixel matches. However, in scenes with weak textures and occlusions, visually similar patches produce ambiguous responses. While recent methods exchange contextual information between image and point features to alleviate this ambiguity, they overlook the topological consistency among neighboring correspondences. In this paper, we propose IHF-ReLoc, an instance-aware hierarchical framework for 2D-3D visual relocalization, which progressively refines matching from instance-level associations to fine-grained point-to-patch correspondences. Specifically, IHF-ReLoc consists of two core modules, namely Scene Graph Matching (SGM) and Candidate Graph Reasoner (CGR). SGM performs matching over 2D and 3D instance graphs and exploits instance topology to enhance feature discriminability, thereby reducing cross-modal ambiguity. The CGR models candidate point-to-patch correspondences as graph nodes and their local geometric relations as edges. By propagating geometric information among neighboring candidates, CGR suppresses visually similar but incorrect matches. Experiments show that IHF-ReLoc achieves state-of-the-art performance and remains robust to temporal scene changes without relying on pose priors obtained through image retrieval.