2D-SuGaR: Surface-Aware Gaussian Splatting for Geometrically Accurate Mesh Reconstruction
3D Gaussian Splatting enables the reconstruction of a volumetric scene representation from multi-view images that allows for real-time novel-view point synthesis, however, it struggles with recovering an accurate surface geometry. While 2D Gaussian Splatting (2DGS) addresses this through surface-aligned primitives, its performance depends critically on the initialization quality. Reliance on Structure-from-Motion (SfM) limits the initialization flexibility as well. In this work, we present two key contributions to enhance 2DGS and the extraction of a clean surface mesh. First, we incorporate monocular depth and normal priors for robust initialization, coupled with a clustering-based pruning strategy to eliminate degenerate Gaussians. Second, we introduce a joint mesh-Gaussian refinement similar to SuGaR, that relaxes the strict 2D constraint by transitioning to 3D primitives, providing stronger training signals. Evaluated on the DTU dataset, our method achieves state-of-the-art mesh reconstruction with a Chamfer Distance of 0.67, outperforming prior methods.
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