Confidence-Based Mesh Extraction from 3D Gaussians

Recently, 3D Gaussian Splatting (3DGS) greatly acceleratedmesh extraction from posed images due to its explicit representation andfast software rasterization. While the addition of geometric losses andother priors has improved the accuracy of extracted surfaces, mesh ex-traction remains difficult in scenes with abundant view-dependent effects.To resolve the resulting ambiguities, prior works rely on multi-view tech-niques, iterative mesh extraction, or large pre-trained models, sacrificingthe inherent efficiency of 3DGS. In this work, we present a simple and ef-ficient alternative by introducing a self-supervised confidence frameworkto 3DGS: within this framework, learnable confidence values dynam-ically balance photometric and geometric supervision. Extending ourconfidence-driven formulation, we introduce losses which penalize per-primitive color and normal variance and demonstrate their benefits tosurface extraction. Finally, we complement the above with an improvedappearance model, by decoupling the individual terms of the D-SSIMloss. Our final approach delivers state-of-the-art results for unboundedmeshes while remaining highly efficient.