The Resource Machine learning methods for 3D object classification and segmentation, by Truc Duc Le

Machine learning methods for 3D object classification and segmentation, by Truc Duc Le

Label
Machine learning methods for 3D object classification and segmentation
Title
Machine learning methods for 3D object classification and segmentation
Statement of responsibility
by Truc Duc Le
Creator
Contributor
Author
Degree supervisor
Subject
Genre
Language
eng
Summary
Object understanding is a fundamental problem in computer vision and it has been extensively researched in recent years thanks to the availability of powerful GPUs and labelled data, especially in the context of images. However, 3D object understanding is still not on par with its 2D domain and deep learning for 3D has not been fully explored yet. In this dissertation, I work on two approaches, both of which advances the state-of-the-art results in 3D classification and segmentation. The first approach, called MVRNN, is based multi-view paradigm. In contrast to MVCNN which does not generate consistent result across different views, by treating the multi-view images as a temporal sequence, our MVRNN correlates the features and generates coherent segmentation across different views. MVRNN demonstrated state-of-the-art performance on the Princeton Segmentation Benchmark dataset. The second approach, called PointGrid, is a hybrid method which combines points and regular grid structure. 3D points can retain fine details but irregular, which is challenge for deep learning methods. Volumetric grid is simple and has regular structure, but does not scale well with data resolution. Our PointGrid, which is simple, allows the fine details to be consumed by normal convolutions under a coarser resolution grid. PointGrid achieved state-of-the-art performance on ModelNet40 and ShapeNet datasets in 3D classification and object part segmentation
Cataloging source
MUU
http://library.link/vocab/creatorName
Le, Truc Duc
Degree
Ph. D.
Dissertation note
Thesis
Dissertation year
2018.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorName
Duan, Ye
Label
Machine learning methods for 3D object classification and segmentation, by Truc Duc Le
Instantiates
Publication
Note
  • Field of study: Computer science
  • Dr. Ye Duan, Thesis Supervisor
  • Includes vita
Bibliography note
Includes bibliographical references (pages 116-140)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
1098195751
Extent
1 online resource (xv, 141 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (some color)
Specific material designation
remote
System control number
(OCoLC)1098195751
Label
Machine learning methods for 3D object classification and segmentation, by Truc Duc Le
Publication
Note
  • Field of study: Computer science
  • Dr. Ye Duan, Thesis Supervisor
  • Includes vita
Bibliography note
Includes bibliographical references (pages 116-140)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
1098195751
Extent
1 online resource (xv, 141 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (some color)
Specific material designation
remote
System control number
(OCoLC)1098195751

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