Modeling and rendering from multiple views

  • Authors:
  • Wai-Kuen Chan;Jian Yao

  • Affiliations:
  • The Chinese University of Hong Kong (People's Republic of China);The Chinese University of Hong Kong (People's Republic of China)

  • Venue:
  • Modeling and rendering from multiple views
  • Year:
  • 2006

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Abstract

The objective of this thesis is to model and render complex scenes or objects from multiple images taken from different viewpoints. Two approaches to achieve this objective were investigated in this thesis. The first one is for known objects with prior geometrical models, which can be deformed to match the objects recorded in multiple input images. The second one is for general scenes or objects without prior geometrical models. The first approach, described in the first part of this thesis, studies 3D face modeling from multi-views. Today human face modeling and animation techniques are widely used to generate virtual characters and models. Such characters and models are used in movies, computer games, advertising, news broadcasting and other activities. We propose an efficient method to estimate the poses, the global shape and the local structures of a human head recorded in multiple face images or a video sequence by using a generic wireframe face model. Based on this newly proposed method, we have successfully developed a pose invariant face recognition system and a pose invariant face contour extraction method. The second approach, described in the second part of this thesis, investigates 3D modeling and rendering for general complex scenes. The entertainment industry touches hundreds of millions of people every day, and synthetic pictures and 3D reconstruction of real scenes, often mixed with actual film footage, are now common place in computer games, sports broadcasting, TV advertising and feature films. A series of techniques has been developed to complete this task. First, a new view-ordering algorithm was proposed to organize and order an unorganized image database. Second, a novel and efficient multiview feature matching approach was developed to calibrate and track all views. Finally, both match propagation based and Bayesian based methods were developed to produce 3D scene models for rendering. The proposed algorithms in this thesis were tested on many real and synthetic data. The experimental results illustrate their efficiency and limitations.