Data-driven Shape Analysis and Synthesis

05 Apr
Thursday, 04/05/2012 12:00pm to 1:00pm
Seminar

Evangelis Kalogerakis
Stanford University
Computer Science Department

Computer Science Building, Room 151

Faculty Host: Rui Wang

The emergence of modern geometry acquisition devices, such as the Kinect, and the appearance of large-scale shape repositories, such as the Google Warehouse, are revolutionizing computer graphics, making three-dimensional content ubiquitous. The need for algorithms that understand and intelligently process 3D shapes is thus greater than ever. In this talk, I will describe a new generation of algorithms that analyze and synthesize complex three-dimensional shapes. The algorithms are based on probabilistic models that reason about both geometry and semantics. In contrast to traditional approaches that consider individual shapes in isolation and require laborious hand-tuning, these algorithms learn from collections of shapes. Specifically, I will present a data-driven shape segmentation technique that outperforms all previous approaches to shape segmentation. I will then describe a shape synthesis technique that can construct high-quality novel shapes from complex domains, such as aircraft, ships, and furniture. Finally, I will discuss new opportunities for geometry processing and 3D modeling enabled by these algorithms.

Bio:
Evangelos Kalogerakis is a postdoctoral researcher in computer science at Stanford University. His research deals with the development of computer graphics techniques that support human creativity and automate complex visual content processing tasks for novice users, scientists and artists. He is particularly interested in developing machine learning algorithms for 3D content analysis and synthesis. He earned a PhD from the department of Computer Science at the University of Toronto. He has been awarded with the NSERC Alexander Graham Bell research fellowship.

A reception will be held at 3:40 in the atrium, outside the presentation room.