CMPUT 615 - 3D Computer Vision

Overview

Central to Computer Vision, Computer Graphics and Image Processing are the mathematical models governing image formation and methods for processing and recovering information based on these.

Computer vision is about making interpretations of what's seen from (possibly many) 2D images. Here we study 3D computer vision, which focuses on how to make use of the spatial and temporal coherence imposed by camera geometry to reconstruct a 3D geometric model from e.g. a moving video camera, stereo camera rig or multiple views from a still camera. As a contrast image processing, pattern recognition and other image analysis often focus on 2D processing, while here we focus on the 3D aspects. In Computer Graphics, one renders 2D images from a 3D model, and the basic mathematics is the same, but the process is a forward process (and hence easier).

The main feature of this course is a solid treatment of geometry to reach and understand the modern non-Euclidean (projective) formulation of camera imaging. This theory found its form and dominated the computer vision conferences in the past decade. However, we warm up with some easier topics in mainly 2D processing for tracking before tacking the more challenging geometry. Finally, we cover recent developments in using variational methods and PDE's to represent and recover surfaces, which is currently a very hot topic in the imaging research literature. Applications of the mathematical techniques are interspersed at appropriate course moments.

Objectives

  • Understand the basics of imaging processing
  • Understand how temporal constraints in video, for example, can be used to track object and form in a coherent interpretation of motions
  • Mathematically understand the relation between the 3D world and it's projection in 2D images and learn how to use these to reconstruct a 3D scene model from several 2D images
  • Use the physics of interaction between light and material to deduce surface normals
  • Be able to apply the variational framework developed above to solve a variety of medical imaging tasks

Course Work

  • Labs
  • Projects
  • Presentations
  • Midterms
  • Final Exam
Course Details
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