Title: Image-based Modeling

Abstract: Image-based modeling is the confluence of Computer Vision and Computer
Graphics. In the first part of the talk, I will review the state of the
art of the three dimensional reconstruction from images or structure
from motion. I will focus on the uncalibrated framework and the
quasi-dense approach that we have developed for many years. The
quasi-dense approach is unique in that it is the most suitable for
building up the object modeling applications. In the second part of the
talk, I will present a series of modeling applications we have been
pursuing in the recent years. I will start with smooth surface modeling
from images, then move to the prior-based hair and tree modeling. I
will finish with the most recent building modeling for digital city
applications.

A short biography of Long Quan:

Long Quan received the Ph.D. degree in Computer Science from INPL,
France, in 1989. Before joining the Department of Computer Science at
the Hong Kong University of Science and Technology (HKUST) in 2001, He
has been a French CNRS senior research scientist at INRIA in Grenoble
since 1990. His research interests focus on 3D reconstruction, structure
from motion, and image-based modeling.

He has served as an Associate Editor of IEEE Transactions on Pattern
Analysis and Machine Intelligence (PAMI) and a Regional Editor of Image
and Vision Computing Journal (IVC). He is on the editorial board of the
International Journal of Computer Vision (IJCV), the Electronic Letters
on Computer Vision and Image Analysis (ELCVIA), Machine Vision and
Applications (MVA) and Foundations and Trends in Computer Graphics and
Vision. He has served and contributed to International Conference on
Computer Vision (ICCV), European Conference on Computer Vision (ECCV),
and IEEE Computer Vision and Pattern Recognition (CVPR) and IAPR
International Conference on Pattern Recognition (ICPR). Among others, he
is a General Chair of ICCV 2011 in Barcelona.

He is the founding director of the HKUST Center for Visual Computing and
Image Science, and a Fellow of the IEEE Computer Society.