SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
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.
我们提出了一种从3D高斯溅射中精确且极快提取网格的方法。高斯溅射最近变得非常流行，因为它产生逼真的渲染，同时训练速度比神经辐射场（NeRFs）快得多。然而，从数百万个微小的3D高斯中提取网格是具有挑战性的，因为这些高斯在优化后往往是无序的，而且迄今为止还没有提出方法。我们的第一个关键贡献是一个正则化项，它鼓励高斯与场景表面良好对齐。然后，我们引入了一种利用这种对齐从高斯中提取网格的方法，使用泊松重建，这种方法快速、可扩展且保留细节，与通常用于从神经SDF中提取网格的行进立方体算法形成对比。最后，我们引入了一种可选的细化策略，将高斯绑定到网格的表面，并通过高斯溅射渲染联合优化这些高斯和网格。这使得通过操作网格而不是高斯本身，使用传统软件轻松编辑、雕刻、装配、动画、合成和重新照明高斯成为可能。与神经SDF上的最先进方法相比，我们的方法在几分钟内就可以检索到这样一个可编辑的网格用于逼真渲染，同时提供更好的渲染质量。