CHS: Small: Inverse Methods for Computer Graphics Material Appearance Design

CHS：小：计算机图形材料外观设计的逆向方法

基本信息

批准号：
2007283
负责人：
Holly Rushmeier
金额：
$ 49.89万
依托单位：
Yale University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007283&HistoricalAwards=false
关键词：
CHS Small Inverse Methods Computer

项目摘要

Material appearance is a critical feature of visual design systems in the service of a wide range of industrial needs, from creating augmented/virtual reality (AR/VR) training environments to designing physical products to specifying the appearance of characters and objects in film and games. Visual accuracy is needed for reliable training, and intuitive tools are needed to minimize the time required to bring new systems online. But while design of the geometry of physical products using computational tools is well-established, an unresolved problem is that the appearance of objects shown in computer design systems cannot reliably be matched in physical production. This project will develop tools for appearance design that create accurate simulations and are easy to use. Project outcomes will have broad impact, by enabling systems to accelerate job training, by making design for manufacture efficient, and by expanding access to production tools.This project will study appearance space to create a new type of material appearance design system. But studying the full range of material appearance is a grand challenge, so rather than attempting to develop a general theory, appearance space will be studied in three contexts: the space of particular material classes; the space of procedural models encoded as node graphs; and the space of physically realizable bi-scale materials. In the context of material classes, image examples of classes of materials (e.g., grass, shiny metals, silk fabric) will be gathered and classified by unsupervised learning techniques to discover the structure of each class and the overlap in appearance classifications. In the context of node graphs used for appearance design, existing graphs for material classes will be collected to find types and structures to drive the design of an automatic system for node graph generation. And in the context of bi-scale material appearance, classes of meso-scale geometries and micro-scale variations will be studied to characterize the range of appearance they can produce. The three studies will be brought together in a system that takes examples of distant and close appearance, along with additional user-supplied class qualifiers, and creates new node types and node graph structures to produces a tunable model for the user to edit as needed.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.

从创建增强/虚拟现实（AR/VR）培训环境到设计物理产品到指定电影和游戏中字符的外观，物质外观是视觉设计系统的关键特征，以满足各种工业需求。可靠的培训需要视觉准确性，并且需要直观的工具才能最大程度地减少将新系统列入在线所需的时间。但是，尽管使用计算工具的物理产品的几何形状的设计是良好的，但尚未解决的问题是，计算机设计系统中显示的对象的外观在物理生产中无法可靠地匹配。该项目将开发用于外观设计的工具，以创建准确的模拟且易于使用。项目成果将通过使系统能够通过提高生产的设计以及扩展对生产工具的访问来加速工作培训，从而产生广泛的影响。该项目将研究外观空间，以创建一种新型的材料外观设计系统。但是，研究各种材料外观是一个巨大的挑战，因此，将在三种情况下研究外观空间，而不是试图发展一般理论：特定物质类别的空间；编码为节点图的过程模型的空间；以及可实现的双尺度材料的空间。在材料类的背景下，将通过无监督的学习技术收集和分类材料类别（例如草，闪亮的金属，丝绸面料）的图像示例，以发现每个类别的结构以及外观分类的重叠。在用于外观设计的节点图的上下文中，将收集现有的材料类图形，以查找类型和结构来驱动节点图生成的自动系统的设计。在双层材料外观的背景下，将研究中尺度几何形状和微尺度变化的类别，以表征它们可以产生的外观范围。 The three studies will be brought together in a system that takes examples of distant and close appearance, along with additional user-supplied class qualifiers, and creates new node types and node graph structures to produces a tunable model for the user to edit as needed.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.