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.

翻译：我们提出了一种从三维高斯泼溅中精确且极速提取网格的方法。高斯泼溅近期因比神经辐射场训练更快且能生成逼真渲染而广受欢迎，然而从数百万个微小三维高斯体中提取网格极具挑战性，因为这些高斯体在优化后往往呈现无序状态，且目前尚无相关方法提出。本文的第一项关键贡献是引入正则化项，促使高斯体与场景表面良好对齐。随后我们提出一种利用这种对齐性通过泊松重建从高斯体提取网格的方法——该方法快速、可扩展且能保留细节，与通常从神经符号距离函数提取网格的Marching Cubes算法形成鲜明对比。最后，我们引入可选的优化策略，将高斯体绑定至网格表面，并通过高斯泼溅渲染联合优化高斯体与网格。该技术使得用户能够通过操作网格而非高斯体自身，在传统软件中轻松实现高斯体的编辑、雕刻、骨骼绑定、动画制作、合成及重光照。使用我们的方法在数分钟内即可获得可用于逼真渲染的可编辑网格，而最先进的神经符号距离函数方法需要数小时，且本方法能提供更优的渲染质量。