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://imagine.enpc.fr/~guedona/sugar/

翻译：我们提出了一种方法，能够从3D高斯泼溅中实现精确且极速的网格提取。高斯泼溅因相比神经辐射场训练速度更快且能生成逼真渲染效果而近来广受欢迎，然而从数百万微小3D高斯中提取网格仍具挑战性，因为这些高斯在优化后往往结构无序，且至今尚未有解决方案提出。我们的首个关键贡献是提出一项正则化项，促使高斯与场景表面良好对齐。随后，我们引入一种利用该对齐性的方法，通过泊松重建从高斯中提取网格——该技术快速、可扩展且保留细节，这与通常应用于神经有符号距离函数网格提取的移动立方体算法形成鲜明对比。最后，我们提出一项可选优化策略：将高斯绑定至网格表面，并通过高斯泼溅渲染联合优化这些高斯与网格本身。这使得用户能通过传统软件操控网格而非高斯本身，轻松实现编辑、雕刻、绑定、动画、合成及重光照等操作。相较于当前基于神经有符号距离函数的最优方法需耗时数小时，我们的方法仅需数分钟即可获得可编辑网格并实现逼真渲染，且渲染质量更优。项目页面：https://imagine.enpc.fr/~guedona/sugar/