3D Functionality Analysis for Shape Modeling via Partial Matching
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In 3D shape modeling, making sure that the modeled shapes are functional can largely facilitate the modeling process, since then the user is able to create more realistic shapes. In this thesis, we present an analysis method for evaluating the functionality of 3D shapes, especially hybrid shapes with multiple functionalities. Our method is based on functionality partial matching, which localizes the functionality analysis down to the partial shape level. We show that functionality partial matching enables functionality analysis for hybrid shapes. Moreover, we incorporate functionality partial matching into an evolutionary shape modeling framework, which evolves an initial set of shapes through crossover operations at the level of shape parts, making the evolutionary process functionality-aware. We show that our functionality-aware model evolution can produce a large and diverse population of functionally plausible hybrid shapes.
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Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2020
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guan-3dfunctionalityanalysisforshapemodelingvia.pdf | 2023-05-05 | Public | Download |