Date of Award
5-2024
Degree Type
Honors College Thesis
Academic Program
Computer Science BS
Department
Computing
First Advisor
Bo Li, Ph.D.
Advisor Department
Computing
Abstract
This project presents a comprehensive exploration into semantics-driven 3D scene retrieval, aiming to bridge the gap between 2D sketches/images and 3D models. Through four distinct research objectives, this project endeavors to construct a foundational infrastructure, develop methodologies for quantifying semantic similarity, and advance a semantics-based retrieval framework for 2D scene sketch-based and image-based 3D scene retrieval. Leveraging WordNet as a foundational semantic ontology library, the research proposes the construction of an extensive hierarchical scene semantic tree, enriching 2D/3D scenes with encoded semantic information. The methodologies for semantic similarity computation utilize this semantic tree to bridge the semantic disparity between 2D sketches/images and 3D models, enhancing retrieval performance. Furthermore, the project proposes a semantics-driven framework for 2D scene sketch-based and image-based 3D scene retrieval, aiming to unlock new opportunities for applications spanning virtual reality, 3D entertainment, and autonomous systems. Overall, this thesis contributes valuable insights and methodologies to the field of semantics-driven 3D scene retrieval, laying a solid foundation for future advancements and interdisciplinary collaborations whilst promoting research development in the developing realm of visual computing.
Copyright
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Recommended Citation
Gleason, Natalie, "Learning Scene Semantics for 3D Scene Retrieval" (2024). Honors Theses. 982.
https://aquila.usm.edu/honors_theses/982