# GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction arXiv: https://arxiv.org/abs/2411.19454 ยท HTML: https://mlsanity.com/paper/2411.19454/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 64 references that are in the map. - First posted on arXiv 29 Nov 2024. ## Builds on (most cited first, 18 of 18) - [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021) - [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023) - [SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes](https://mlsanity.com/paper/2312.14937/) (2023) - [PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction](https://mlsanity.com/paper/2406.06521/) (2024) - [GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces](https://mlsanity.com/paper/2311.17977/) (2023) - [Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes](https://mlsanity.com/paper/2404.10772/) (2024) - [Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting](https://mlsanity.com/paper/2401.01339/) (2024) - [Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle](https://mlsanity.com/paper/2312.03431/) (2023) - [VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction](https://mlsanity.com/paper/2402.17427/) (2024) - [GaussianPro: 3D Gaussian Splatting with Progressive Propagation](https://mlsanity.com/paper/2402.14650/) (2024) - [VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality](https://mlsanity.com/paper/2401.16663/) (2024) - [NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance](https://mlsanity.com/paper/2312.00846/) (2023) - [Differentiable Rendering: A Survey](https://mlsanity.com/paper/2006.12057/) (2020) - [Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction](https://mlsanity.com/paper/2208.12697/) (2022) - [GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction](https://mlsanity.com/paper/2403.16964/) (2024) - [3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting](https://mlsanity.com/paper/2404.00409/) (2024) - [SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior](https://mlsanity.com/paper/2403.20079/) (2024) - [FastHuman: Reconstructing High-Quality Clothed Human in Minutes](https://mlsanity.com/paper/2211.14485/) (2022) ## Built on by (most cited first, 19 of 19) - [Radiance Meshes for Volumetric Reconstruction](https://mlsanity.com/paper/2512.04076/) (2025) - [SOF: Sorted Opacity Fields for Fast Unbounded Surface Reconstruction](https://mlsanity.com/paper/2506.19139/) (2025) - [VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment](https://mlsanity.com/paper/2510.11473/) (2025) - [GS-RoadPatching: Inpainting Gaussians via 3D Searching and Placing for Driving Scenes](https://mlsanity.com/paper/2509.19937/) (2025) - [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025) - [TRAN-D: 2D Gaussian Splatting-based Sparse-view Transparent Object Depth Reconstruction via Physics Simulation for Scene Update](https://mlsanity.com/paper/2507.11069/) (2025) - [MetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes](https://mlsanity.com/paper/2511.19172/) (2025) - [Gaussian Set Surface Reconstruction through Per-Gaussian Optimization](https://mlsanity.com/paper/2507.18923/) (2025) - [GeoSplat: A Deep Dive into Geometry-Constrained Gaussian Splatting](https://mlsanity.com/paper/2509.05075/) (2025) - [GSPlane: Concise and Accurate Planar Reconstruction via Structured Representation](https://mlsanity.com/paper/2510.17095/) (2025) - [TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting](https://mlsanity.com/paper/2512.06269/) (2025) - [Geometry Gaussians: Decoupling Appearance and Geometry in Gaussian Splatting](https://mlsanity.com/paper/2606.05124/) (2026) - [When 3D Gaussian Splatting Recovers Real Surfaces](https://mlsanity.com/paper/2608.30054/) (2026) - [A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction](https://mlsanity.com/paper/2507.11321/) (2025) - [GS-2M: Material-aware Gaussian Splatting for High-fidelity Mesh Reconstruction](https://mlsanity.com/paper/2509.22276/) (2025) - [SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction](https://mlsanity.com/paper/2603.05152/) (2026) - [Sparse-View Surface Reconstruction using Gaussian Splatting through High-Confidence Depth Propagation with Normal Priors](https://mlsanity.com/paper/2607.03765/) (2026) - [Confidence matters: Leveraging Multi-view Geometric Priors for GS-based Reconstruction](https://mlsanity.com/paper/2608.06117/) (2026) - [Gaussian Sculpting: End-to-End Controllable Surface Reconstruction via Field Optimization](https://mlsanity.com/paper/2608.10602/) (2026) ## Abstract (arXiv) 3D Gaussian Splatting has achieved impressive performance in novel view synthesis with real-time rendering capabilities. However, reconstructing high-quality surfaces with fine details using 3D Gaussians remains a challenging task. In this work, we introduce GausSurf, a novel approach to high-quality surface reconstruction by employing geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene. We observe that a scene can be mainly divided into two primary regions: 1) texture-rich and 2) texture-less areas. To enforce multi-view consistency at texture-rich areas, we enhance the reconstruction quality by incorporating a traditional patch-match based Multi-View Stereo (MVS) approach to guide the geometry optimization in an iterative scheme. This scheme allows for mutual reinforcement between the optimization of Gaussians and patch-match refinement, which significantly improves the reconstruction results and accelerates the training process. Meanwhile, for the texture-less areas, we leverage normal priors from a pre-trained normal estimation model to guide optimization. Extensive experiments on the DTU and Tanks and Temples datasets demonstrate that our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.