[CPL Seminar]
[
Schedule]
[
Jan 9]
[
Jan 16]
[
Jan 23]
[
Jan 30]
[
Feb 6]
[
Feb 20]
[
Feb 25]
[
Mar 7 Shum]
[
Mar 7 Szeliski]
[
Mar 13]
[
Mar 20]
[
Mar 27]
[
April 3]
[
April 10]
[
April 17]
[
April 24]

Mar 27

Katsushi Ikeuchi
Institute of Industrial Science, the University of Tokyo, Tokyo, Japan

Online Slides

Modelling Cultural Heritage through Observation

This talk presents an overview of our efforts in modeling cultural heritage
through observation. These efforts span three aspects: how to create
geometric models of cultural heritage; how to create photometric models of
cultural heritage; and how to integrate such virtual heritages with real
scenes. For geometric model creation, we have developed a two-step method:
simultaneous alignment and volumetric view merging. For photometric model
creation, we have developed the eigen-texture rendering method, which
automatically creates photo-realistic models by observing the real objects.
For the integration of virtual objects with real scenes, we have developed a
method that renders virtual objects based on real illumination distribution.
We have applied these component techniques to constructing a multimedia
model of the great Buddha in Kamakura, and demonstrated their effectiveness.

Dr. Katsushi Ikeuchi is a Professor at the Institute of Industrial Science,
the University of Tokyo, Tokyo, Japan. He received the Ph.D. degree in
Information Engineering from the University of Tokyo, Tokyo, Japan, in 1978.
After working at the Artificial Intelligence Laboratory at Massachusetts
Institute of Technology, the Electrotechnical Laboratory of the Ministry of
International Trade and Industries, and the School of Computer Science,
Carnegie Mellon University, he joined the University of Tokyo, in 1996. He
has received several awards, including the David Marr Prize in
computational vision, and IEEE R&A K-S Fu memorial best transaction paper
award. In addition, in 1992, his paper, "Numerical Shape from Shading and
Occluding Boundaries," was selected as one of the most influential papers to
have appeared in Artificial Intelligence Journal within the past ten years.