Monday 27 January • 8:30 – 12:45
Course Length: 4 hours
Course Level: Introductory
Instructor: Gady Agam, Illinois Institute of Technology

Learning Outcomes
This course enables the attendee to:

• Describe fundamental concepts in 3D imaging.
• Develop algorithms for 3D model reconstruction from 2D images.
• Incorporate camera calibration into your reconstructions.
• Classify the limitations of reconstruction techniques.
• Use industry standard tools for developing 3D imaging applications.

The purpose of this course is to introduce algorithms for 3D structure inference from 2D images. In many applications, inferring 3D structure from 2D images can provide crucial sensing information. The course begins by reviewing geometric image formation and mathematical concepts that are used to describe it, and then discusses algorithms for 3D model reconstruction.

The problem of 3D model reconstruction is an inverse problem in which we need to infer 3D information based on incomplete (2D) observations. We discuss reconstruction algorithms, which utilize information from multiple views. Reconstruction requires the knowledge of some intrinsic and extrinsic camera parameters, and the establishment of correspondence between views. We discuss algorithms for determining camera parameters (camera calibration) and for obtaining correspondence using epipolar constraints between views. The course also introduces relevant 3D imaging software components available through the industry standard OpenCV library.

Intended Audience
Engineers, researchers, and software developers, who develop imaging applications and/or use camera sensors for inspection, control, and analysis. The course assumes a basic working knowledge concerning matrices and vectors.

Gady Agam is an associate professor of computer science at the Illinois Institute of Technology. He is the director of the Visual Computing Lab at IIT which focuses on imaging, geometric modeling, and graphics applications. He received his PhD from Ben-Gurion University (1999).