Pfister Lab
Principal Investigator
School of Engineering & Applied Sciences, Harvard University
33 Oxford Street, MD 227
Cambridge,
MA
02138
+1 (617) 496 8269
Hanspeter Pfister's research lies at the intersection of visualization, computer graphics, and computer vision. It spans a wide range of topics, including visualization, computational photography, point-based graphics, appearance modeling, 3D television, and face recognition.
He received his Ph.D. in Computer Science in 1996 from the State University of New York at Stony Brook and his M.S. in Electrical Engineering from the Swiss Federal Institute of Technology (ETH) Zurich, Switzerland, in 1991.
Prior to his appointment at Harvard, Pfister worked for 11 years at Mitsubishi Electric Research Laboratories (MERL) where he was most recently Associate Director and Senior Research Scientist. He was the chief architect of VolumePro, Mitsubishi Electric’s award-winning real-time volume rendering hardware for PCs.
Research Staff
Students
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Recent Publications
IEEE International Conference on Computer Vision (ICCV), (2009)
Proceedings of ACM SIGGRAPH, Volume 28, (2009)
Conference on Computer Vision and Pattern Recognition (CVPR), (2009)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008., p.1-8, (2008)
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Featured projects
Face Scanning
The goal of this project is to build high-quality statistical models for human faces. The applications of such models include face recognition, digital face aging, user interfaces, and face synthesis. We have built a scanning system that is able to capture images of human more »
Time-Lapse Video Factorization
We developed a method for converting time-lapse photography captured with outdoor cameras into Factored Time-Lapse Video (FTLV): a video in which time appears to move faster (i.e., lapsing) and where data at each pixel has been factored into shadow, illumination, and reflectance components. The factorization allows a user to easily relight the scene, recover a portion of the scene geometry (normals), and to perform advanced image editing operations. Our method is easy to implement, robust, and provides a compact representation with good reconstruction characteristics. more »