Accurate and Complete Surface Reconstruction from 3D Gaussians via Direct SDF Learning

• URL: http://arxiv.org/abs/2509.07493v2
• Date: Sun, 21 Sep 2025 06:09:22 GMT
• Title: Accurate and Complete Surface Reconstruction from 3D Gaussians via Direct SDF Learning
• Authors: Wenzhi Guo, Bing Wang,
• Abstract summary: 3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for photorealistic view synthesis.<n>We propose DiGS, a unified framework that embeds Signed Distance Field (SDF) learning directly into the 3DGS pipeline.<n>We show that DiGS consistently improves reconstruction accuracy and completeness while retaining high fidelity.
• Score: 5.604709769018076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for photorealistic view synthesis, representing scenes with spatially distributed Gaussian primitives. While highly effective for rendering, achieving accurate and complete surface reconstruction remains challenging due to the unstructured nature of the representation and the absence of explicit geometric supervision. In this work, we propose DiGS, a unified framework that embeds Signed Distance Field (SDF) learning directly into the 3DGS pipeline, thereby enforcing strong and interpretable surface priors. By associating each Gaussian with a learnable SDF value, DiGS explicitly aligns primitives with underlying geometry and improves cross-view consistency. To further ensure dense and coherent coverage, we design a geometry-guided grid growth strategy that adaptively distributes Gaussians along geometry-consistent regions under a multi-scale hierarchy. Extensive experiments on standard benchmarks, including DTU, Mip-NeRF 360, and Tanks& Temples, demonstrate that DiGS consistently improves reconstruction accuracy and completeness while retaining high rendering fidelity.

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