Hierarchical Visual Relocalization with Nearest View Synthesis from Feature Gaussian Splatting

Visual relocalization is a fundamental task in the field of 3D computer vision, estimating a camera’s pose when it revisits a previously known scene. While point-based hierarchical localization methods have shown strong scalability and efficiency, they are often limited by sparse image observations and weak feature matching. In this work, we propose SplatHLoc, a novel hierarchical visual relocalization framework that uses Feature Gaussian Splatting as the scene representation. For feature matching, we observe that Gaussian-rendered features and those extracted directly from images exhibit different strengths across the two-stage matching process: the former performs better in the coarse stage, while the latter proves more effective in the fine stage. Therefore, we introduce a hybrid feature matching strategy, enabling more accurate and efficient pose estimation. Extensive experiments on both indoor and outdoor datasets show that SplatHLoc significantly enhances the robustness of visual relocalization, setting a new state-of-the-art. The code will be released upon acceptance.