Ray-SCR: Generalizing Scene Coordinate Regression Across Camera Models

Jaemin Cho, Oleksii Nasypanyi, Francois Rameau

Among visual localization strategies, scene coordinate regression (SCR) has emerged as an attractive approach for its compactness, accuracy, and speed. Despite these advantages, SCR models generalize poorly across substantially different camera models, such as omnidirectional or strongly rectified imagery. This limitation arises because, changes in field of view, lens distortion, or rectification-induced resampling can make the same scene content appear differently depending on its pixel position, degrading localization accuracy. To cope with this limitation, we introduce Ray-SCR, a lightweight extension to existing SCR architectures that conditions local visual features on their calibrated viewing rays and refines neighboring ray-conditioned features with a shallow local Transformer. We further formulate supervision directly in ray space and use sparse SfM points as metric anchors that link observations across views and camera models. Taken together, these components enable a single scene representation built from a panoramic mapping sequence to accurately localize query images captured with very different cameras, including pinhole, fisheye, and omnidirectional cameras. We…