Staff View
From photos to 3D design: a product shape family design and modeling framework with image-based reconstruction and 3D convolutional neural networks

Descriptive

TitleInfo
Title
From photos to 3D design: a product shape family design and modeling framework with image-based reconstruction and 3D convolutional neural networks
Name (type = personal)
NamePart (type = family)
Jin
NamePart (type = given)
Tian
NamePart (type = date)
1991-
DisplayForm
Tian Jin
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Gea
NamePart (type = given)
Hae Chang
DisplayForm
Hae Chang Gea
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Lee
NamePart (type = given)
Howon
DisplayForm
Howon Lee
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Bai
NamePart (type = given)
Xiaoli
DisplayForm
Xiaoli Bai
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Guo
NamePart (type = given)
Weihong
DisplayForm
Weihong Guo
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Current development of new product and product variation is largely driven by the increasingly sophisticated and demanding customers, and product variety plays a crucial role to gain customer satisfaction at a wider range. Our research focuses on the shape variety of product, which is inherently linked to both functional and aesthetic variety. The challenge that we are addressing here is the low conceptual design efficiency with cross-professional and back-and-forth communication due to limited design visualization tools during converting demands into ideas and then into 3D models.
In our research, a learning-based product shape family design and modeling framework is developed, which integrated image-based reconstruction and 3D shape learning to simplify the modeling process but meanwhile providing abundant flexibilities for 3D shape variation and thus improving conceptual design efficiency. With the reconstruction system and learning system, raw model generating process can be as simple as taking photos around design or redesign targets and making selections from predicted models. Two subsystems are developed for the reconstruction process, a Structure from Motion system that recovers camera motion and a sparse structure, and a Multi-View Stereo system generating denser matches and thus denser point clouds. The incremental reconstruction strategy is adopted, and an initial pair selection strategy and an extrinsic matrix correction measure are derived and utilized to provide better initial camera motion estimation for any further global adjustment optimization. Our experiments shows improved accuracy and robustness of the reconstruction system. To understand what is in the reconstructed point cloud, a 3D convolutional neural network model is constructed to identify 3D shapes from point cloud data. The model is trained by labeled pre-processed point cloud data obtained from both reconstruction and CAD file sampling. The preprocessing includes normalization, down-sampling, manually labeling, and voxelization. The voxel representation made it possible to use convolutional-styled learning method. And the two-layered configuration of Convolution-ReLU-Max Pooling proved to have good performance in classifying 3D shapes.
A new concept of Product Shape Family is defined, and a hierarchical-structured library of product shape family tree is proposed so that classification can be done at different level with a smaller number of candidate classes. Chain rule is used to calculate the possibility at certain shape family and this way the user can be provided with multiple best guesses and select from them at different family generations. A modularized model is defined within every product shape family including internal modules that form the product shape platform and external modules that are optional. The editing process of the selected shape family model is simply selecting desired module and edit shape with pushing and pulling operations on predefined control points. The new design could form a new family branch in the library, or it can be sampled and preprocessed in the same manner as training data and fed back to training process along with newly scanned point clouds.
Two examples are presented to demonstrate performance of the framework from reconstructing 3D point clouds from photos of common design objects to classifying their shape families, and to design with modularized 3D models. Our design and modeling framework reconciles the dilemma between functionality and user-friendliness of 3D modeling tools. In this way, new shape ideas or variants can be easily modeled and visualized in real time and in 3D for non-CAD-users, which improves communication efficiency and hence improves conceptual design efficiency.
Subject (authority = RUETD)
Topic
Mechanical and Aerospace Engineering
Subject (authority = local)
Topic
3D-CNN
Subject (authority = LCSH)
Topic
Three-dimensional imaging
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10171
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xii, 133 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-peb1-4e81
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
JIN
GivenName
TIAN
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-08-16 11:49:53
AssociatedEntity
Name
TIAN JIN
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.7
ApplicationName
Microsoft® Word for Office 365
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-08-16T11:42:45
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-08-16T11:42:45
Back to the top
Version 8.3.13
Rutgers University Libraries - Copyright ©2021