A Generalized Proceduralization Framework for Urban Models with Applications in Procedural Modeling, Synthesis, and Reconstruction

Ilke Demir, Purdue University

Abstract

The technological developments in the last century have carried us from a few pixels per screen to infinite worlds. Consecutively, recent bottleneck in graphics industry has been changing from the “means” to the “objects”, in other words, from the technology to the content. In particular, architectural models have always played an important role in areas like computer graphics, virtual environments, and urban planning. Because of the proliferation of such areas, the demand for city-scale 3D urban models has significantly increased, as well as the availability of 3D data sets of buildings and other urban structures. One option is to manually create such content, however humans are expensive and slow, and more and more are needed as the content to be created explodes for movies, games, and visualizations. Another option is to efficiently re-use the existing large set of 3D polygonal models available from public databases, created by scans, images, time-of-flight cameras, manual modeling, etc. However the results of these approaches usually lack high-level grouping or segmentation information which hampers efficient re-use and synthesis. Procedural models are known to be an effective solution to create massive amount of content from powerful and compact parameterized representations. While procedural modeling provides compelling architectural structures, creating detailed and realistic buildings needs time and extensive coding as well as significant domain expertise. In other words, procedural modeling is a solution for content creation, however lack of artistic control and the need for domain expertise make it challenging. Observing the content creation problem and the challenges of procedural modeling, we identified and developed a set of proceduralization tools, that convert existing models into an easy to manipulate procedural form and allow quick synthesis of visually similar objects. The central idea of our research is that we can automate and assist modeling and creation tasks by proceduralization of existing models such as architectural meshes, building point clouds, or textured urban areas. The geometrical and visual information hidden in already existing models actually contain high-level semantic and structural information, so a proceduralization framework can reveal such information for a variety of purposes. Our proceduralization framework for urban spaces allows the re-use of existing models in various formats by first conducting shape analysis methods on such models to find and exploit the repetitions and similarities revealing the hidden high-level structural information, and then using grammar discovery methods on those components to extract a representative grammar (or a procedural form) of the model. We have shown that the structural information and repetitions inside already existing models can be exploited to obtain the high-level representation hidden in such models for making the design and modeling of urban content more efficient and intuitive. Initial applications of our work enables the benefits of procedural modeling on existing models such as compression, rendering, and content generation. Additionally, we have proposed new applications of such procedural representations, including reconstruction of incomplete models, geo-localization within urban areas, and structure preserving synthesis systems. We expect our effort will enlighten the artists' and designers' creation process by converting the existing modeling tools into faster, easier to implement, more interactive, and more intuitive procedural modeling systems, using the power of proceduralization.

Degree

Ph.D.

Advisors

Aliaga, Purdue University.

Subject Area

Computer science

COinS