Multi-View Consistency

Multi-view consistency concerns whether images or predictions from different viewpoints describe the same underlying scene, object, or event. Common approaches use geometric correspondences, shared 3D representations, cross-view attention, and consistency losses; generative systems also use diffusion priors and distillation to coordinate synthesis and editing. The ideas support 3D reconstruction and novel-view synthesis, multi-view perception, clustering, and spatial reasoning, including sparse, occluded, or dynamic settings. A central challenge is that stable predictions can still be wrong, so evaluation must test both cross-view agreement and geometric or semantic accuracy while preserving useful view-specific details.