DGS: Dual-Evidence Integration for Accurate Sparse-View Surface Reconstruction with Gaussian Splatting

Accurate surface reconstruction with 3D Gaussian Splatting (3DGS) remains challenging under sparse views, where limited multi-view observations provide insufficient geometric constraints. Recent methods alleviate this issue by supervising reconstruction with aligned depth predictions from foundation models. However, these predictions typically lack explicit dense multi-view matching and can be suboptimal where reliable photometric correspondences are available. More importantly, their depth reliability is inherently range-dependent: aligned VGGT depth exhibits smoothly varying bias across surfaces, while local depth variations remain substantially more accurate and degrade gradually with spatial distance. Motivated by this, we propose DGS, a dual-evidence integration framework for sparse-view surface reconstruction. Rather than directly using predicted depth as supervision, DGS combines two complementary forms of geometric evidence: point evidence from multi-view photometric matching provides accurate absolute anchor depths at reliable correspondences, while edge evidence from local log-depth differences of VGGT captures dense local depth variations. A global integration couples the two, extending anchor depths through local edge relations while constraining accumulated drift. We further introduce prior-based aggregation and anchor depth refinement to improve the coverage and accuracy of point evidence under sparse views. Experiments on DTU and Tanks and Temples demonstrate state-of-the-art surface reconstruction quality.