Scale-Consistent Feature Fields for Retrieval-Free Visual Relocalization

Feature-based scene representation plays a critical role in visual relocalization, enabling robots and augmented reality systems to estimate their positions and orientations without global positioning. However, this task is often undermined by significant variations in viewpoint-induced scale and scene appearance. Recently, Gaussian splatting has emerged as a promising scene model due to its explicit geometry and real-time rasterization. In this paper, we propose ScalePlace, a retrieval-free framework that learns Scale-Consistent Gaussian Feature Fields (SCGFF) for retrieval-free centimeter-level visual relocalization. Specifically, our framework performs pose estimation through an integrated sparse-to-dense pipeline built upon a rasterizable Gaussian field, comprising three key aspects: First, Descriptor-Attached Gaussian Representation (DAGR) yields a view-dependent feature field that can be both sparsely sampled for initialization and densely rendered for refinement; Second, Multi-Resolution Feature Supervision (MRFS) facilitates cross-scale consistency through Gaussian rendering at multiple resolutions with aligned photometric and SuperPoint objectives; Third, Scale-Aware Pose Estimation (SAPE) empowers the sparse matcher and dense alignment with resolution-consistent primitives to support both initialization and iterative refinement without retrieval or pose priors. Extensive evaluations across five public datasets demonstrate that the proposed approach substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments.