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3D object reconstruction using multiple views |
Kim, Donghoon
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THESIS 9687 3D object modelling from multiple view images has recently been of
increasing interest in computer vision. Two techniques, Visual Hull
and Photo Hull, have been extensively studied in the hope of developing
3D shape from multiple views. These early methods have the
advantage that they do not require pre-processing procedures such
as feature selection and matching, which fail when images are of low
resolution. One drawback of these two methods is their discrete formulation,
which is demanding of memory and limits the type of optimisation
methods that can be used. This study proposes a continuous
formulation in contrast to the discrete formulations typical of these
earlier methods, and aims to robustly reconstruct the 3D shape and
colour of an object seen in a multi-view system. The use of a continuous
formulation based on kernel density estimates enables us to
define a gradient ascent algorithm (e.g. a mean shift algorithm) to
recover the 3D shape and colour. Moreover, we propose to include
prior information in this continuous modelling to improve the quality
of the reconstruction. The proposed approach has several advantages:
it is less memory demanding, the resulting algorithm is suitable for
parallel processing, and it recovers concavities that are usually lost
when estimating shape from silhouettes with the standard visual hull
method.
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Keyword(s):
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Statistics, Ph.D.; Ph.D. Trinity College Dublin |
Publication Date:
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2011 |
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Type:
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Doctoral thesis |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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Trinity College Dublin |
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Citation(s):
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Donghoon Kim, '3D object reconstruction using multiple views', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2011, pp 174 |
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Publisher(s):
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Trinity College (Dublin, Ireland). School of Computer Science & Statistics |
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Supervisor(s):
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Dahyot, Rozenn |
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First Indexed:
2016-11-08 06:34:17 Last Updated:
2017-04-26 10:35:22 |
