Figure: (a) 3D range scan of
Park Avenue and 70th street (scanner is a the center of the
intersection). Bird's-eye view. Data gathered by the Leica
ScanStation2 of our laboratory. (b) 3D texture-mapped model of
building at CCNY. Camera positions are shown (see IJCV 2008
paper in publications
Online classification of objects in urban scene (red is
Recent advances in computer hardware have made possible the
efficient rendering of realistic 3D models in inexpensive
PCs, something that was possible with high end visualization
workstations only a few years ago. This class will cover the field
of 3D Photography -the process of automatically creating 3D
texture mapped models of objects- in detail. We will concentrate on the topics at the
intersection of Computer
Vision and Computer Graphics that are relevant to
acquiring, creating, and representing 3D models of small objects
or large urban areas. Many very interesting research questions
need to be answered. For example: how do we acquire real shapes?
how do we represent geometry? can we detect similarities between
shapes? can we detect symmetries within shapes? how do we register
3D geometry with color images?, etc. Applications
that benefit by this technology include: historical
preservation, urban planning, google-type maps, architecture, navigation, virtual reality,
e-commerce, digital cinematography, computer games, just to name a few.
The core of the class will
be a set of presentations of research papers.
There will be a weekly class, with presentations by the
instructor. The presentations will introduce the basic concepts
and techniques of the field. Each
student will present one or two assigned topics in class.
Outstanding projects can lead to successful PhD theses, and to research paper
The grade will
be based upon the following:
50% for group
or individual projects, 30% for presentation(s) and 20% for
Students need to be familiar
with at least one of the following topics: Image/Pixel Processing,
Computer Vision, Computer Graphics, or Robotics. A prerequisite
can be waived by permission of the instructor. Please contact
Prof. Stamos directly if you feel you do not have the required
- Acquiring images: 2D and 3D sensors (digital cameras and laser
- 3D- and 2D- image registration.
- Geometry: representation of 3D models, simplification of 3D
models, detection of symmetry.
- Rendering 3D models.
- Image based rendering.
- Texture mapping.
- Object classification.
- Neural Networks for 2D and 3D detection and classification
This class will be based on
recent publications and recent workshops. A set of seminars,
books, and journals are provided for your reference.
Computer Vision: Algorithms and Applications, Richard
Szeliski, 2010: Online Version
Introductory Techniques for 3-D Computer Vision.
EmanueleTrucco and Alessandro Verri. Prentice Hall, 1998.
Robot Vision. B. K. P. Horn, The MIT Press, 1998 (12th
Three-Dimensional Computer Vision: A Geometric Viewpoint.
Olivier Faugeras, The MIT Press, 1996.
An Invitation to 3-D Vision. Yi Ma, Stefano Soatto, Jana
Kosecka, S. Shankar Sastry. Springer-Verlag, 2004.
Computer Vision A Modern approach.
S. Forsyth, Jean Ponce. Prentice Hall 2003.
Computer Vision. Linda Shapiro and George Stockman. Prentice
Graphics, Principles and Practice. Foley, van Dam, Feiner,
and Hudges. Addison-Wesley, 1997.
Computer Vision and
3D Computer Graphics. Alan Watt. Addison-Wesley,
OpenGL Programming Guide. Mason Woo, Jackie Neider,
Tom Davis. Addison-Wesley, 1998.
International Journal on Computer Vision.
Computer Vision and Image Understanding.
IEEE Trans. on Pattern Analysis and Machine Intelligence.