Representations and Techniques for 3D Object Recognition and Scene Interpretation

Representations and Techniques for 3D Object Recognition and Scene Interpretation

Derek Hoiem, Silvio Savarese,
ISBN: 9781608457281 | PDF ISBN: 9781608457298
Copyright © 2011 | 169 Pages | Publication Date: 01/01/2011

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One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning.

The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition.

Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes.

Table of Contents

Background on 3D Scene Models
Single-view Geometry
Modeling the Physical Scene
Categorizing Images and Regions
Examples of 3D Scene Interpretation
Background on 3D Recognition
Modeling 3D Objects
Recognizing and Understanding 3D Objects
Examples of 2D 1/2 Layout Models
Reasoning about Objects and Scenes
Cascades of Classifiers
Conclusion and Future Directions

About the Author(s)

Derek Hoiem, University of Illinois at Urbana-Champaign
Derek Hoiem is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC). Before joining the UIUC faculty in 2009, Derek completed his Ph.D. in Robotics at Carnegie Mellon University in 2007 and was a postdoctoral fellow at the Beckman Institute from 2007-2008. Derek's work on scene understanding and object recognition was recognized with a 2006 CVPR Best Paper award, a 2008 ACM Doctoral Dissertation Award honorable mention, and a 2011 NSF CAREER award.

Silvio Savarese, University of Michigan
Silvio Savarese is an Assistant Professor of Electrical Engineering at the University of Michigan, Ann Arbor. After earning his Ph.D. in Electrical Engineering from the California Institute of Technology in 2005, he joined the University of Illinois at Urbana-Champaign from 2005-2008 as a Beckman Institute Fellow. He was a recipient of an NSF Career Award in 2011 and a Google Research Award in 2010. In 2002 he was awarded the Walker von Brimer Award for outstanding research initiative.


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