CHS: Small: Functional Proceduralization of 3D Geometric Models

Purdue University, West Lafayette IN

Investigators

Abstract

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.