CHS: Small: Functional Proceduralization of 3D Geometric Models

虽然计算能力和数字存储可用性在近几十年来有了巨大的增长，但创建有用的3D数字内容仍然很困难。 虽然我们可以在互联网上找到大量现有的3D模型，但这些模型通常在几何上是杂乱无章的，这严重限制了它们的实用性。 这项研究的基本愿景是通过将非结构化数据转换为有组织且易于编辑的表示，促进大型复杂3D模型的半自动，可控内容创建和编辑，用于数字仿真，可视化，娱乐，教育和文化遗产。 考虑到这一目标，该项目将创建数字化工具，将作为输入提供的几何模型程序化为一组有意义的规则及其参数，以便模型紧凑，可以以直观的方式进行更改，并且可以快速渲染。 项目成果预计将在计算机图形应用中产生广泛的影响，跨越各种领域。这项工作的基础方法是找到一个程序表示（即，算法、规则集和参数值），其可以在考虑其功能的同时生成给定的输入模型。 该方法的一个关键的新奇是自动生产的程序表示和学习的功能，以及对象的语义结构。 该项目将探索在编程语言（如Python和Java Script）中生成过程表示，并使用新的过程表示，如自适应几何图形语法或扩展到形状和分裂语法。 因此，该方法将有助于内容创建，通过启用相同对象的功能有效的变体的生成，暴露用于执行高级或低级更改的参数，并完成具有合理细节的对象。 这项工作将进行三个主要组成部分：（发现输入模型的原子或终端元素）;模式化（找到并枚举共同用于产生输入对象的参数化规则和/或参数值）;和合成（提供算法和工具以使得能够高效且直观地生成一个或多个输入对象的合成变体）该奖项反映了NSF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。

While computational power and digital storage availability have increased tremendously in recent decades, the creation of useful 3D digital content is still difficult. Although we can find huge collections of existing 3D models on the Internet, these models are typically geometrically disorganized which severely limits their utility. The vision underlying this research is to facilitate semi-automatic, controllable content creation and editing of large and complex 3D models for use in digital simulation, visualization, entertainment, education, and cultural heritage, by converting unstructured data into organized and easily editable representations. With that goal in mind, this project will create digital tools that proceduralize a geometric model provided as input into a set of meaningful rules and their parameters so that the model is compact, can be altered in an intuitive way, and is fast to render. Project outcomes are expected to have broad impact in computer graphics applications spanning a wide variety of domains.The methodology underlying this work is to find a procedural representation (i.e., algorithm, rule set, and parameter values) that can generate a given input model while considering its function. A key novelty of the approach is the automatic production of a procedural representation and the learning of the function as well as the semantic structure of the object. The project will explore generation both of procedural representations in programming languages such as Python and Java Script and by using novel procedural representations such as adaptive geometric graph grammars or extensions to shape and split grammars. As a result, the methodology will help content creation by enabling the generation of functionally-valid variants of the same object, exposing parameters for performing high-level or low-level alterations, and completing the object with plausible details. The work will proceed with three main components: atomization (discovery of the atomic, or terminal, elements of the input model); patternization (finding and enumerating the parameterized rules and/or the parameter values that are collectively used to produce the input object); and synthetization (providing algorithms and tools to enable the efficient and intuitive generation of synthetic variations of the input object or objects).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.