Towards Learning of a Joint Geometry-Structure Manifold for Shape Exploration

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2017-12-23
Authors/Contributors
Abstract
We present a first attempt at producing a continuous generative model of 3D objects from a joint representation that incorporates the discrete structural variability as well as the continuous geometric variability that are often present in collections of man-made shapes. Starting from a set of compatibly segmented shapes, our main contribution consists in demonstrating the construction of the joint representation. Then, by using Gaussian Process learning to produce a predictive manifold from the joint representation, we investigate its capabilities and limitations for reproducing and synthesizing new shapes.
Document
Identifier
etd9972
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Copyright is held by the author.
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This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Zhang, Hao
Thesis advisor: Huang, Hui
Member of collection
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etd9972_JVargasTrujillo.pdf 37.92 MB