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MS_THESIS_AHMED_ABDELREHEEM_MAY_2022.pdf
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Description:
MS Thesis
Embargo End Date:
2023-09-08
Type
ThesisAuthors
Abdelreheem, Ahmed
Advisors
Wonka, Peter
Committee members
Viola, Ivan
Hadwiger, Markus

Program
Computer ScienceDate
2022-05-31Embargo End Date
2023-09-08Permanent link to this record
http://hdl.handle.net/10754/681019
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At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2023-09-08.Abstract
In this thesis, we introduce a 3D Shape Grammar for generating man-made objects and an optimization algorithm to fit the structure and parameters of the grammar to a given point cloud. While simple grammars, e.g. for bounding box arrangements, have been used to define prior knowledge for reconstruction before, we take this approach to the next level, by attempting to fit complex grammars with many rules, many parameters, and curved geometric primitives. To tackle this challenge, we propose two major components. First, we propose a new shape grammar framework suitable to describe man-made objects. We also develop a complex rule set for one particular shape class, chairs, to highlight the expressiveness of the shape grammar framework. Our rule set can represent up to 55.7k unique structures. An essential design requirement of the grammar was to make it differentiable for a shape-fitting application. Second, we propose a probabilistic framework to fit the shape of grammar to a point cloud. We combine probabilistic sampling of the grammar structure with differentiable optimization of grammar parameters to that effect.Citation
Abdelreheem, A. (2022). Modeling and Fitting 3D Shapes via Shape Grammar [KAUST Research Repository]. https://doi.org/10.25781/KAUST-40747ae974a485f413a2113503eed53cd6c53
10.25781/KAUST-40747