We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. 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, rigging, animating, compositing and relighting of the Gaussians using traditional softwares 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 methods on neural SDFs, while providing a better rendering quality. Our project page is the following: https://anttwo.github.io/sugar/

翻译：我们提出了一种方法，能够从三维高斯溅射中实现精确且极快速的网格提取。高斯溅射近年来变得非常流行，因为它在训练速度上显著快于NeRF的同时，还能实现逼真的渲染效果。然而，从数百万个微小三维高斯中提取网格极具挑战性，因为这些高斯点在优化后往往呈现无序状态，且目前尚未有方法提出解决方案。我们的第一个关键贡献是引入了一项正则化项，旨在促使高斯点与场景表面良好对齐。随后，我们提出了一种利用这种对齐性，通过泊松重建从高斯点中提取网格的方法——该方法快速、可扩展且能保留细节，与通常用于从神经符号距离函数中提取网格的Marching Cubes算法形成对比。最后，我们引入了一种可选的优化策略，将高斯点绑定到网格表面，并通过高斯溅射渲染联合优化这些高斯点与网格。这使得用户能够通过操作网格而非高斯点本身，利用传统软件轻松实现对高斯点的编辑、雕刻、绑定、动画、合成及重新照明。与现有基于神经SDF的最先进方法需要数小时相比，我们的方法仅需数分钟即可获得这种可编辑网格以实现逼真渲染，同时提供更优的渲染质量。项目页面如下：https://anttwo.github.io/sugar/