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.

我们提出了一种方法，可以从 3D 高斯散点图中精确且非常快速地提取网格。该方法使用高斯散点图进行实际渲染，相较于 NeRFs 具有更快的训练速度。我们首先引入了一个鼓励高斯散点图与场景表面对齐的正则项，然后利用此对齐性通过 Poisson 重建方法从高斯散点图中提取网格，该方法快速、可扩展且能保留细节信息，与通常用于从神经隐式函数中提取网格的 Marching Cubes 算法形成对比。最后，我们引入了可选的优化策略，通过高斯散点图渲染将高斯散点图与网格表面绑定，在传统软件中通过操纵网格而非高斯散点图本身实现了易于编辑、雕刻、绑定、动画制作、合成和调光。与基于神经隐式函数的最先进方法相比，我们的方法可在几分钟内获取逼真渲染所需的可编辑网格，而不是使用数小时。同时，我们的方法提供更好的渲染质量。