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

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Thesis type
(Thesis) M.Sc.
Date created
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.
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Supervisor or Senior Supervisor
Thesis advisor: Zhang, Hao
Thesis advisor: Huang, Hui
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