RAGR: Region-Adaptive Group Routing for Multi-View 3D Reconstruction

Recent visual geometry foundation models recover camera poses, depth, and dense 3D structure from multiple images, yet a single global prediction model struggles to accurately reconstruct every local region. We investigate whether applying multi-choice, region-level corrections on top of a global prediction can further enhance reconstruction quality. We propose RAGR, a region-adaptive group routing framework for post-prediction refinement. RAGR partitions an initial 3D reconstruction into spatial groups supported by multi-view co-visibility, and employs a lightweight router to decide for each group whether to retain the shared global prediction or apply a compact correction learned from diverse teacher models. In zero-shot evaluations, RAGR improves upon the shared global prediction on ETH3D and outperforms full-frame geometric experts on Tanks and Temples (TNT). Furthermore, while RAGR remains competitive with full-frame experts on ETH3D, its improvement over the global baseline does not transfer stably to TNT. These results demonstrate the feasibility and advantages of localized post-prediction refinement, while identifying transferable action selection as the core requirement for effective spatial routing.