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dc.contributor.advisorFunkhouser, Thomas-
dc.contributor.authorSizikova, Elena-
dc.contributor.otherComputer Science Department-
dc.description.abstractShape synthesis is an important area of computer vision and graphics that concerns creation of new shapes and reconstruction from partial data. Its goal is to learn a model that can generate shapes within an object category suitable for novel shape creation, interpolation, completion, editing, and other geometric modeling applications. Existing tools learn shape properties from large collections of shapes. Although these methods have been very successful at learning how to synthesize the coarse shapes of objects in categories with highly diverse shapes, they have not always produced examples that reconstruct important structural elements of a shape. In this thesis, I describe how structure can be incorporated into the synthesis process, and how it can be used to improve generative models. First, I introduce a template-dened skeleton structure for learning a part-aware generative model in typography, where the shapes have a known structure and can be explained by a small number of templates. Next, I present a scenario of noisy archaeological wall painting (fresco) reconstruction from eroded fragments, where there is no well-dened structure and exponentially many arrangement possibilities in this case, I present a cluster evaluation function that guides the assembly process and encourages selection of good clusters. Finally, I describe a semantic landmark-based structure and how it can be used to improve a generative model of examples with extremely varied topology by means of a geometric shape-structure consistency loss. Through exploration of each type of structure, I show how reasoning with proposed structures helps synthesize more accurate and realistic shapes. I also propose a fully automatic framework for font completion. Finally, I design a genetic algorithm for wall painting reconstruction and propose an iterative outlier detection technique based on the eigenvector method.-
dc.publisherPrinceton, NJ : Princeton University-
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: <a href=> </a>-
dc.subject3D reconstruction-
dc.subjectcomputer vision-
dc.subjectshape analysis-
dc.subjectshape synthesis-
dc.subject.classificationComputer science-
dc.titleShape Synthesis Using Structure-Aware Reasoning-
dc.typeAcademic dissertations (Ph.D.)-
Appears in Collections:Computer Science

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