Hallucinate, Filter, and Refine: Occluded Surface Reconstruction with Generative Priors

Reconstructing reliable 3D surfaces from sparse input views remains challenging when large scene regions are unobserved due to occlusions. Generative models can expand view coverage by synthesizing missing observations, but generated views often contain hallucinated structures, pose errors, and appearance drift that can introduce unreliable supervision. We propose Hallucinate, Filter, and Refine, a reliability aware framework that exploits generative priors while controlling their uncertainty throughout reconstruction. We first establish a geometric anchor from sparse inputs and synthesize multiple candidate view sequences along augmented camera trajectories. We then progressively filter generated observations at both sequence and view levels using patch-wise visual compatibility with input views and consistency with observed geometry. Our Auxiliary Control Module (ACM) estimates patch wise reliability based on feature consistency and reconstruction difficulty. ACM regulates photometric supervision by suppressing uncertain generated regions while preserving reliable information. Since uncertain generated regions can be difficult to identify before sufficient geometric convergence, ACM adaptively activates reliability weighting based on reconstruction progress. This design preserves valid information during early optimization while reducing the influence of uncertain generated regions as reconstruction stabilizes. Experiments demonstrate improved occluded surface recovery while maintaining competitive rendering quality under sparse observations.