Electronic Theses and Dissertations

Date

2023

Document Type

Thesis

Degree Name

Master of Science

Department

Electrical & Computer Engineering

Committee Chair

Dr. Mohammed Yeasin

Committee Member

Dr. Faruk Ahmed

Committee Member

Dr. Vida Abedi

Committee Member

Dr. Aaron L. Robinson

Abstract

Performances of Deep Learning (DL) based methods for 3D reconstruction are becoming at par or better than classical computer vision techniques. Learning requires data with proper annotations. While images have a standardized representation, there is currently no widely accepted format for efficiently representing 3D output shapes. The challenge lies in finding a format that can handle the high-resolution geometry of any shape while also being memory and computationally efficient. Therefore, most advanced learning-based 3D reconstructions are restricted to a certain domain. In this work, we compare the performance of different output representations for 3D reconstruction in different contexts including objects or natural scenes, full human body to human body parts reconstruction. Despite substantial progress in the semantic understanding of the visual world, there are few methods that can reconstruct from a single view for a s large set of objects. Our the objective is to investigate methods to reconstruct a wider variety of object categories in 3D and aim to achieve accurate 3D reconstruction at both object and scene levels. In this work, we compare the performance of different output representations for 3D reconstruction in such a way that will give us implicit and smooth output representation of complex geometry of 3D from RGB images, DICOM (Digital Imaging and Communications in Medicine) formatted MRI breast images and images from a wild environment in terms of input using the Deep Learning methods and available 3D processing applications (MeshLab, 3D Slicer, and Mayavi).

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to ProQuest

Notes

Open Access

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