GenAnchor: Observation-Anchored Progressive Reconstruction with Generative Priors for Sparse-View Gaussian Splatting

Recent sparse-view 3D reconstruction methods exploit generative priors to com- plete under-observed scene content. However, generated views are not necessarily grounded in the available input evidence and may therefore conflict with real ob- servations, degrading the appearance and geometry of reliably reconstructed re- gions. To address this issue, we present GenAnchor, an observation-anchored progressive reconstruction framework that preserves reliably observed content while selectively applying generative supervision to uncertain and unobserved re- gions. Specifically, we first employ coverage-aware trajectory planning to explore inadequately reconstructed regions. Each planned view is then partitioned into preservation, refinement, and generation regions using a tri-state masking strat- egy, thereby adapting generative supervision to the reconstruction status of each region. Subsequently, we recover depth from the repaired RGB sequence using the planned camera poses and depth anchors from real observations, preserving observed geometry while guiding depth inference in generated regions. The re- sulting RGB-D views are integrated into the global scene representation, whose updated state guides the next round of trajectory planning and view repair, thereby forming a progressive reconstruction loop. Extensive experiments on multiple widely used public datasets demonstrate that our method consistently improves scene completeness while better preserving reliably observed regions, achieving state-of-the-art performance in both appearance fidelity and geometric accuracy.