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Local Refinement for Stereo Regularization

På svenska

Author

Summary, in English

Stereo matching is an inherently difficult problem due to ambiguous and noisy texture. The non-convexity and non- differentiability makes local linear (or quadratic) approximations poor, thereby preventing the use of standard local descent methods. Therefore recent methods are predominantly based on discretization and/or random sampling of some class of approximating surfaces (e.g. planes). While these methods are very efficient in generating a rough surface estimate, via either fusion of proposals or label propagation, the end result is usually not as smooth as desired. In this paper we show that, if the objective function is decomposed correctly, local refinement of candidate solutions can be performed using an ADMM approach. This allows searching over more general function classes, thereby resulting in visually more appealing smooth surface estimations.

Publishing year

2014

Language

English

Pages

4056-4061

Publication/Series

Pattern Recognition (ICPR), 2014 22nd International Conference on

Document type

Conference paper

Publisher

IEE

Topic

  • Mathematics
  • Computer Vision and Robotics (Autonomous Systems)

Conference name

22nd International Conference on Pattern Recognition (ICPR 2014)

Conference date

2014-08-24 - 2014-08-28

Conference place

Stockholm, Sweden

Status

Published

Research group

  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISSN: 1051-4651