SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering

By Antoine Guédon, Vincent Lepetit. Computer Vision and Pattern Recognition, 2023

864 citations recorded by the source corpus.

We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting [15]. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D Gaussians as these Gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the Gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction, which is fast, scalable, and preserves details, in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally, we introduce an optional refinement strategy that binds Gaussians to the surface of the mesh, and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing, sculpting, animating, and relighting of the Gaussians by manipulating the mesh instead of the Gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method, compared to hours with the state-of-the-art method on SDFs, while providing a better rendering quality.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e3f80d950e6f841bd7eea4c24d4e1e5aa2bd85c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Bingyi Kang
2. Jiashi Feng

In Computer Graphics and Computer-Aided Design

1. Jiashi Feng

In Computer Vision and Pattern Recognition

1. J. Liew
2. Bingyi Kang
3. Jiashi Feng
4. Donny Y. Chen
5. Yin Yang
6. Zeshun Zong
7. Xuan Li
8. Tianyi Xie

Citations in the corpus

• A Survey on 3D Gaussian Splatting (2024, neutral): “These results indicate the effectiveness of introducing additional properties or structured information to model the deformation of Gaussians so as to model the scene dynamics.”
• PGSR: Planar-Based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction (2024, neutral): “SuGaR [21] proposed a method to extract Mesh from 3DGS.”
• Depth Anything 3: Recovering the Visual Space from Any Views (2025, positive): “To reduce the trade-off between accurate surface geometry and rendering quality [29], we predict an additional depth offset in the GS-DPT head.”
• PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics (2023, methodology): “Additionally, incorporating geometry-aware 3DGS reconstruction methods [9] could enhance generative dynamics.”