Collaborative Research: HCC: Medium: Neural Materials for Realistic Computer Graphics

现实主义一直是计算机图形学的一个重要目标，不仅因为它使图像更有说服力，虚拟环境更有沉浸感，而且因为当渲染用于做出现实世界的决定时，准确性很重要。逼真的图像是通过使用模型来模拟场景中表面的光反射的物理过程，而目前的反射模型虽然对于光滑、均匀的材料是准确的，但对于具有详细表面结构的材料是相当不准确的。该项目探索了一种全新的表面反射建模方法，通过对表面的许多详细测量来学习特定类别材料的特征反射模式。这些新模型将彻底改变计算机图形学在电影制作、工业设计、市场营销、广告、建筑、虚拟现实和家庭改造等应用中的材料建模方式，从而产生广泛的影响。这项研究的中心是建立更适合描述精细细节的模型。当前的“基于物理的材料”本质上是参数反射模型，其参数由纹理图调制。它们可以匹配大规模的聚合行为，但不能匹配近尺度真实反射率的复杂性。该项目探索了一种基于神经函数逼近器的全新方法来表示材料。这项工作将不仅仅是简单地将反射率附加到不透明的物体上，而是通过开发薄的神经反射率场来适应模糊、半透明和未建模的几何细节。新的神经材料将是灵活和通用的，适用于从虚拟环境到视觉效果所需的所有材料，支持从只有少数参考照片的自动外观开发到图像的逆对象建模的应用，结合几何和材料细节。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Realism has always been an important goal of computer graphics, not only because it makes images more convincing and virtual environments more immersive, but because accuracy is important when renderings are used to make real-world decisions. Realistic images are made by using models to simulate the physical process of light reflection from surfaces in the scene, and state-of-the-art reflection models, while accurate for smooth, homogeneous materials, are quite inaccurate for materials with detailed surface structure. This project explores an entirely new way of modeling surface reflection, by learning the characteristic reflectance patterns of particular classes of materials from many detailed measurements of surfaces. These new models will have broad impact by completely transforming the way materials are modeled for computer graphics in applications like moviemaking, industrial design, marketing, advertising, architecture, virtual reality and home remodeling.This research centers around building models that are more appropriate for describing fine-scale detail. Current “physically based materials” are essentially parametric reflectance models with parameters modulated by texture maps. These can match aggregate large-scale behavior, but not the complexities of real reflectance at close scale. This project explores a fundamentally new way to represent materials, based on neural function approximators. The work will go beyond simply attaching reflectance to opaque objects, by developing thin neural reflectance fields that accommodate fuzziness, translucency and unmodeled geometric detail. The new neural materials will be flexible and general for the full range of materials needed from virtual environments to visual effects, supporting applications ranging from automatic look development from only a few reference photographs, to inverse object modeling from images, combining geometric and material detail.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.