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，虚拟环境更身临其境，而且因为当渲染用于做出真实世界的决策时，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制作的，并且最先进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非常不准确的。 该项目探索了一种全新的表面反射建模方法，通过从许多详细的表面测量中学习特定类别材料的特征反射模式。这些新模型将通过彻底改变计算机图形学中材料建模的方式产生广泛的影响，这些应用包括电影制作、工业设计、营销、广告、建筑、虚拟现实和家庭改造。 当前的“基于物理的材料”本质上是参数反射模型，其参数由纹理贴图调制。这些可以匹配聚合物的大尺度行为，但不是真实的反射率在密切的规模的复杂性。该项目探索了一种基于神经功能近似器的全新材料表示方法。这项工作将超越简单地将反射附加到不透明的物体上，通过开发薄的神经反射场来适应模糊性，透明度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各种材料，支持从仅从几张参考照片自动外观开发到从图像逆向对象建模的应用，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。

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.