AnchorGeo: Confidence-Gated Depth Alignment for Weakly Observed Regions in Gaussian Surface Reconstruction

Reconstructing accurate surfaces from multi-view images with radiance fields has achieved remarkable progress in recent years. However, prevailing approaches, primarily based on Gaussian Splatting, remain unreliable in weakly observed regions, where the photometric loss is nearly flat and geometry drifts freely: self-consistency regularizers cannot resolve such regions, while external priors applied indiscriminately fight the photometric evidence exactly where it is decisive. In this paper, we introduce AnchorGeo, a confidence-gated depth-alignment framework that commits geometry precisely where observations run out. Our key finding is that the reliability of the rendered depth is detectable per pixel: a crossing in front of the transmittance peak extracts a view-independent surface, whereas one behind the peak is view-dependent by construction. To locate trustworthy geometry, we first propose a Transmittance-based Confidence Gate that separates reliable anchors from weak pixels directly at rendering time. Subsequently, Confidence-Anchored Depth Alignment is designed to align a monocular depth prior to the anchored geometry in closed form on each object segment and to pull only the weak pixels toward the aligned target, routing external cues into exactly the regions the images cannot constrain. Extensive experiments on DTU and Tanks-and-Temples demonstrate state-of-the-art surface quality with the largest gains concentrated in weakly observed regions, and a per-point recovery/damage decomposition verifies that these gains come from recovering weak regions rather than damaging well-observed ones.