Entro-Bridge: Entropy-Structured Refinement for Feed-Forward 3D Reconstruction

Recent advances in single-view 3D reconstruction have been enabled by feed-forward and generative models, allowing efficient learning-based inference from incomplete visual observations. However, these models often overlook the spatially heterogeneous reliability of reconstructed geometry: feed-forward predictions may contain reliable observations, uncertain predictions, and unsupported regions, while generative completion may overwrite reliable structures during unseen-region recovery. We formulate this challenge as a reliability-aware stochastic repair problem, where reliable geometry should be preserved, uncertain predictions should be corrected, and unsupported regions should be completed. To solve this problem, we propose Entro-Bridge, an entropy-structured paired Schrodinger Bridge framework that enables selective geometric repair through uncertainty-controlled stochastic transport. Specifically, we introduce an Entropy-structured Reliability-aware Source Boundary (ERSB), which transforms feed-forward predictions into a source distribution with spatially structured entropy: reliable observations are assigned low uncertainty, uncertain predictions allow greater flexibility for correction, and unsupported regions retain greater freedom for completion. During reverse transport, the Reliability-Guided reverse Repair Dynamics (RGRD) carries this reliability structure into the reverse repair process, guiding the correction trajectory toward uncertain regions while preserving reliable geometry. On the ABO and 3D-FRONT datasets, Entro-Bridge consistently outperforms state-of-the-art methods, reducing Chamfer Distance by approximately 13.0 % and 10.3% relative to SOTA methods, with F-scores of 92.0 % and 81.3 %, respectively.