arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.14554cs.CVcs.GR

Gaussian Frosting:支持实时渲染的可编辑复杂辐射场

Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering

LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS(LIGM实验室,巴黎路桥学院,古斯塔夫·埃菲尔大学,法国国家科学研究中心)

Antoine Guédon, Vincent Lepetit

AI总结:

提出Gaussian Frosting,一种基于网格的可编辑复杂辐射场表示方法,通过在基础网格周围构建自适应厚度的高斯层捕捉细节和体积效果,实现实时高质量渲染、编辑与动画。

摘要:

我们提出了Gaussian Frosting,一种基于网格的新颖表示方法,用于高质量渲染和实时编辑复杂的3D效果。我们的方法建立在最近的3D Gaussian Splatting框架之上,该框架优化一组3D高斯函数以从图像中近似辐射场。我们提出首先在优化过程中从高斯函数中提取一个基础网格,然后围绕该网格构建并细化一个具有可变厚度的自适应高斯层,以更好地捕捉表面附近的精细细节和体积效果,如头发或草地。我们将该层称为Gaussian Frosting,因为它类似于蛋糕上的糖霜涂层。材质越模糊,涂层越厚。我们还引入了高斯函数的参数化方法,以强制其保持在涂层层内,并在变形、重新缩放、编辑或动画化网格时自动调整其参数。我们的表示方法允许使用高斯泼溅进行高效渲染,并通过修改基础网格进行编辑和动画化。我们在各种合成和真实场景上展示了该方法的有效性,并表明它优于现有的基于表面的方法。我们将发布我们的代码和一个基于Web的查看器作为额外贡献。我们的项目页面如下:https://anttwo.github.io/frosting/

英文摘要:

We propose Gaussian Frosting, a novel mesh-based representation for high-quality rendering and editing of complex 3D effects in real-time. Our approach builds on the recent 3D Gaussian Splatting framework, which optimizes a set of 3D Gaussians to approximate a radiance field from images. We propose first extracting a base mesh from Gaussians during optimization, then building and refining an adaptive layer of Gaussians with a variable thickness around the mesh to better capture the fine details and volumetric effects near the surface, such as hair or grass. We call this layer Gaussian Frosting, as it resembles a coating of frosting on a cake. The fuzzier the material, the thicker the frosting. We also introduce a parameterization of the Gaussians to enforce them to stay inside the frosting layer and automatically adjust their parameters when deforming, rescaling, editing or animating the mesh. Our representation allows for efficient rendering using Gaussian splatting, as well as editing and animation by modifying the base mesh. We demonstrate the effectiveness of our method on various synthetic and real scenes, and show that it outperforms existing surface-based approaches. We will release our code and a web-based viewer as additional contributions. Our project page is the following: https://anttwo.github.io/frosting/

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。

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