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Richard Szeliski: Reflections on Image-Based Modeling and Rendering

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Published on Apr 19, 2013

Image-based modeling and rendering have been active areas of in computer vision and graphics since the early 1990s. After seminal work in representations and algorithms based on Light Fields and Lumigraphs, the field has seen steady improvements in many of the component technologies, including image-based modeling of 3D proxies, dealing with irregularly sampled data, the incorporation of video, and an interesting interplay with non-photorealistic rendering and computational photography. It has also spawned widely used consumer experiences such as panoramic "VR" photography, street-level (and indoor) immersive tours, and rich 3D navigation of Internet photo collections. In this talk, I review the evolution of this field, tease out some of the common themes and techniques, and speculate on the remaining difficulties and promises in this field, including the handling of reflections and transparent motion that commonly occur in such applications.

Richard Szeliski is a Distinguished Scientist at Microsoft Research, where he leads the Interactive Visual Media Group. He is also an Affiliate Professor at the University of Washington, and is a Fellow of the ACM and IEEE. Dr. Szeliski has done pioneering research in the fields of Bayesian methods for computer vision, image-based modeling, image-based rendering, and computational photography, which lie at the intersection of computer vision and computer graphics. His research on Photo Tourism and Photosynth is an exciting example of the promise of large-scale image-based rendering.

Dr. Szeliski received his Ph.D. degree in Computer Science from Carnegie Mellon University, Pittsburgh, in 1988 and joined Microsoft Research in 1995. Prior to Microsoft, he worked at Bell-Northern Research, Schlumberger Palo Alto Research, the Artificial Intelligence Center of SRI International, and the Cambridge Research Lab of Digital Equipment Corporation. He has published over 150 research papers in computer vision, computer graphics, medical imaging, neural nets, and numerical analysis, as well as the books Computer Vision: Algorithms and Applications and Bayesian Modeling of Uncertainty in Low-Level Vision. He is a Program Committee Chair for CVPR'2013, served as an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and on the Editorial Board of the International Journal of Computer Vision, and is a Founding Editor of Foundations and Trends in Computer Graphics and Vision.

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