Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156658
Title: Reconstruction of 3D mesh from 2D image using deep learning
Authors: Lee, Wonn Jen
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lee, W. J. (2022). Reconstruction of 3D mesh from 2D image using deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156658
Project: SCSE21-0039
Abstract: This paper evaluates the feasibility of deep learning for monocular depth estimation in the reconstruction of 3D meshes. Three deep learning models were used to generate a depth map, and three surface reconstruction algorithms were used to reconstruct the mesh. The different combinations were explored, and the best combination was found to be the supervised deep learning model, Dense-Depth, paired with the surface reconstruction using alpha shapes. The meshes produced were able to capture major features of the scene, but tended to have gaps within the mesh, and the depth of the surface would fluctuate. To create a higher quality mesh, the accuracy and resolution of the depth estimation models would have to be improved first. This final year project is part of research project “Artificial Intelligence for Smart Image Understanding” at Rolls-Royce@NTU Corporate Lab.
URI: https://hdl.handle.net/10356/156658
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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