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Local Refinement for Stereo Regularization

Olsson, Carl LU ; Ulén, Johannes LU and Eriksson, Anders (2014) 22nd International Conference on Pattern Recognition (ICPR 2014) In Pattern Recognition (ICPR), 2014 22nd International Conference on p.4056-4061
Abstract
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... (More)
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. (Less)
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author
organization
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type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Pattern Recognition (ICPR), 2014 22nd International Conference on
pages
6 pages
publisher
IEE
conference name
22nd International Conference on Pattern Recognition (ICPR 2014)
external identifiers
  • wos:000359818004032
  • scopus:84919935988
ISSN
1051-4651
DOI
10.1109/ICPR.2014.695
language
English
LU publication?
yes
id
8daec9d8-859e-4a19-9bd4-2fe42f26d207 (old id 4777616)
alternative location
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6977408
date added to LUP
2015-02-20 13:06:57
date last changed
2017-01-01 05:50:36
@inproceedings{8daec9d8-859e-4a19-9bd4-2fe42f26d207,
  abstract     = {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.},
  author       = {Olsson, Carl and Ulén, Johannes and Eriksson, Anders},
  booktitle    = {Pattern Recognition (ICPR), 2014 22nd International Conference on},
  issn         = {1051-4651},
  language     = {eng},
  pages        = {4056--4061},
  publisher    = {IEE},
  title        = {Local Refinement for Stereo Regularization},
  url          = {http://dx.doi.org/10.1109/ICPR.2014.695},
  year         = {2014},
}