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Variational surface interpolation from sparse point and normal data

Solem, Jan Erik LU ; Aanaes, Henrik and Heyden, Anders LU (2007) In IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1). p.181-184
Abstract
Many visual cues for surface reconstruction from known views are sparse in nature, e.g., specularities, surface silhouettes, and salient features in an otherwise textureless region. Often, these cues are the only information available to an observer. To allow these constraints to be used either in conjunction with dense constraints such as pixel-wise similarity, or alone, we formulate such constraints in a variational framework. We propose a sparse variational constraint in the level set framework, enforcing a surface to pass through a specific point, and a sparse variational constraint on the surface normal along the observed viewing direction, as is the nature of, e.g., specularities. These constraints are capable of reconstructing... (More)
Many visual cues for surface reconstruction from known views are sparse in nature, e.g., specularities, surface silhouettes, and salient features in an otherwise textureless region. Often, these cues are the only information available to an observer. To allow these constraints to be used either in conjunction with dense constraints such as pixel-wise similarity, or alone, we formulate such constraints in a variational framework. We propose a sparse variational constraint in the level set framework, enforcing a surface to pass through a specific point, and a sparse variational constraint on the surface normal along the observed viewing direction, as is the nature of, e.g., specularities. These constraints are capable of reconstructing surfaces from extremely sparse data. The approach has been applied and validated on the shape from specularities problem. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
surface interpolation, multiple view stereo, specularities, shape from, level set method, variational methods, computer vision
in
IEEE Transactions on Pattern Analysis and Machine Intelligence
volume
29
issue
1
pages
181 - 184
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000241988300016
  • scopus:33947219268
ISSN
1939-3539
DOI
10.1109/TPAMI.2007.250610
language
English
LU publication?
yes
id
dc307291-0955-42bd-be44-9cd77c3297cd (old id 685814)
date added to LUP
2008-01-03 08:30:58
date last changed
2017-08-13 04:26:07
@article{dc307291-0955-42bd-be44-9cd77c3297cd,
  abstract     = {Many visual cues for surface reconstruction from known views are sparse in nature, e.g., specularities, surface silhouettes, and salient features in an otherwise textureless region. Often, these cues are the only information available to an observer. To allow these constraints to be used either in conjunction with dense constraints such as pixel-wise similarity, or alone, we formulate such constraints in a variational framework. We propose a sparse variational constraint in the level set framework, enforcing a surface to pass through a specific point, and a sparse variational constraint on the surface normal along the observed viewing direction, as is the nature of, e.g., specularities. These constraints are capable of reconstructing surfaces from extremely sparse data. The approach has been applied and validated on the shape from specularities problem.},
  author       = {Solem, Jan Erik and Aanaes, Henrik and Heyden, Anders},
  issn         = {1939-3539},
  keyword      = {surface interpolation,multiple view stereo,specularities,shape from,level set method,variational methods,computer vision},
  language     = {eng},
  number       = {1},
  pages        = {181--184},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title        = {Variational surface interpolation from sparse point and normal data},
  url          = {http://dx.doi.org/10.1109/TPAMI.2007.250610},
  volume       = {29},
  year         = {2007},
}