Matched, but Where? Query-Conditioned Point Prediction for 3DGS-based Visual Localization

3D Gaussian Splatting (3DGS) has emerged as a powerful scene representation for visual localization, enabling direct matching between 2D image keypoints and 3D Gaussian primitives as well as efficient rendering for pose refinement. Existing direct matching methods typically assign the center of a matched Gaussian to the query keypoint. However, a Gaussian represents a volumetric region, and its center generally differs from the observed scene point. That is to say, a correct Gaussian match can therefore yield an inaccurate 3D coordinate, compromising PnP-based pose estimation. To address this limitation, we propose ReCoLoc, a region-to-coordinate framework that explicitly separates region retrieval from point prediction by equipping Gaussian landmarks with query-conditioned coordinate functions. Specifically, contribution-weighted identity encoding aggregates multiview descriptors using Gaussian rendering contributions, yielding reliable regional identities for retrieval. Query-conditioned coordinate decoding then predicts the corresponding 3D position within the retrieved Gaussian region from the query descriptor, thereby reducing the geometric error introduced by center-based assignment. Considering that retrieval and coordinate prediction both determine correspondence quality, correspondence-aware landmark selection jointly evaluates their reliability when constructing the localization map. Also, we introduce shared-subspace map compression, which jointly encodes identity descriptors and coordinate functions in a common low-dimensional basis, reducing direct-matching map storage substantially with little loss of localization accuracy. Experiments on Cambridge Landmarks, 7-Scenes, and 12-Scenes demonstrate that ReCoLoc outperforms current state-of-the-arts in terms of localization accuracy and recall.