SuGaR：面向高效三维网格重建与高质量网格渲染的表面对齐高斯泼溅
SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
AI总结：
SuGaR通过正则化使三维高斯对齐场景表面，并结合泊松重建实现快速精确的网格提取，再通过高斯与网格联合优化支持可编辑的高质量渲染，显著优于现有神经SDF方法。
摘要：
我们提出了一种方法，能够从三维高斯泼溅（3D Gaussian Splatting）中实现精确且极快的网格提取。高斯泼溅近来变得非常流行，因为它能产生逼真的渲染效果，同时训练速度显著快于NeRF。然而，从数百万个微小的三维高斯中提取网格具有挑战性，因为这些高斯在优化后往往呈现无组织状态，且迄今为止尚未有人提出相应方法。我们的第一个关键贡献是一个正则化项，它促使高斯与场景表面对齐良好。随后，我们引入一种方法，利用这种对齐通过泊松重建（Poisson reconstruction）从高斯中提取网格，该方法快速、可扩展且能保留细节，与通常用于从神经SDF中提取网格的Marching Cubes算法形成对比。最后，我们引入一种可选的细化策略，将高斯绑定到网格表面，并通过高斯泼溅渲染联合优化这些高斯与网格。这使得可以通过操作网格而非高斯本身，使用传统软件对高斯进行便捷的编辑、雕刻、绑定骨骼、动画制作、合成和重新光照。与在神经SDF上需要数小时的最新方法相比，我们的方法在几分钟内即可获取此类可编辑网格并实现逼真渲染，同时提供更好的渲染质量。我们的项目页面如下：https://anttwo.github.io/sugar/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
