CoLoR: The Devil is in Scene Coordinate Regression for Large-Scale Visual Localization

Scene Coordinate Regression (SCR) has emerged as a memory-efficient paradigm for visual localization.While SCR has demonstrated performance comparable to classic feature matching based approaches in small-scale scenes, it has consistently underperformed in large-scale environments.Large-scale localization is hampered by two challenges: sparse co-visibility and local appearance ambiguity.In this work, we propose CoLoR, a novel training framework tailored for large-scale SCR.First, we explicitly and efficiently partition scene points into multi-view and single-view sets and introduce a two-stage bootstrapping paradigm to provide complete and strong supervision for all points.Second, we propose a multi-granularity retrieval feature, which unifies the conventional global and local features as retrieval-oriented representations at the image and pixel levels, respectively, to enforce feature consistency.Our method achieves state-of-the-art performance on multiple challenging large-scale datasets and significantly narrows the accuracy gap with classical feature matching based approaches while retaining a compact map size.