Deformable Surface 3D Reconstruction from Monocular Images

Mathieu Salzmann, Pascal Fua

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English

Springer International Publishing AG
15 December 2010

Pattern recognition; Computer vision; Image processing

Series: Synthesis Lectures on Computer Vision

Being able to recover the shape of 3D deformable surfaces from a single video stream would make it possible to field reconstruction systems that run on widely available hardware without requiring specialized devices. However, because many different 3D shapes can have virtually the same projection, such monocular shape recovery is inherently ambiguous. In this survey, we will review the two main classes of techniques that have proved most effective so far: The template-based methods that rely on establishing correspondences with a reference image in which the shape is already known, and non-rigid structure-from-motion techniques that exploit points tracked across the sequences to reconstruct a completely unknown shape. In both cases, we will formalize the approach, discuss its inherent ambiguities, and present the practical solutions that have been proposed to resolve them. To conclude, we will suggest directions for future research. Table of Contents: Introduction / Early Approaches toNon-Rigid Reconstruction / Formalizing Template-Based Reconstruction / Performing Template-Based Reconstruction / Formalizing Non-Rigid Structure from Motion / Performing Non-Rigid Structure from Motion / Future Directions

By: Mathieu Salzmann, Pascal Fua
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Dimensions: Height: 235mm, Width: 191mm,
Weight: 236g
ISBN: 9783031006821
ISBN 10: 3031006828
Series: Synthesis Lectures on Computer Vision
Pages: 99
Publication Date: 15 December 2010
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Mathieu Salzmann received his B.Sc and M.Sc degrees in computer science in 2004 from EPFL (Swiss Federal Institute of Technology). He obtained his PhD degree in computer vision in 2009 from EPFL. He then joined the International Computer Science Institute and the EECS department at UC Berkeley as a postdoctoral fellow. Recently, he joined TTI Chicago as a Research Assistant Professor. His research interests include non-rigid shape recovery, human pose estimation, and optimization techniques for computer vision.