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View-based 3-D Object Retrieval
1st Edition - December 4, 2014
Authors: Yue Gao, Qionghai Dai
Paperback ISBN:9780128024195
9 7 8 - 0 - 1 2 - 8 0 2 4 1 9 - 5
eBook ISBN:9780128026236
9 7 8 - 0 - 1 2 - 8 0 2 6 2 3 - 6
Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile… Read more
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Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging research topic.
View-based 3-D Object Retrieval introduces and discusses the fundamental challenges in view-based 3-D object retrieval, proposes a collection of selected state-of-the-art methods for accomplishing this task developed by the authors, and summarizes recent achievements in view-based 3-D object retrieval. Part I presents an Introduction to View-based 3-D Object Retrieval, Part II discusses View Extraction, Selection, and Representation, Part III provides a deep dive into View-Based 3-D Object Comparison, and Part IV looks at future research and developments including Big Data application and geographical location-based applications.
Systematically introduces view-based 3-D object retrieval, including problem definitions and settings, methodologies, and benchmark testing beds
Discusses several key challenges in view-based 3-D object retrieval, and introduces the state-of-the-art solutions
Presents the progression from general image retrieval techniques to view-based 3-D object retrieval
Introduces future research efforts in the areas of Big Data, feature extraction, and geographical location-based applications
Graduate students, academic researchers and professionals interested in or doing work in 3-D object retrieval
Acknowledgments
Preface
Part I: The Start
Introduction
Chapter 1: Introduction
Abstract
1.1 The Definition of 3DOR
1.2 Model-Based 3DOR Versus View-Based 3DOR
1.3 The Challenges of V3DOR
1.4 Summary of Our Work
1.5 Structure of This Book
1.6 Summary
Chapter 2: The Benchmark and Evaluation
Abstract
2.1 Introduction
2.2 The Standard Benchmarks
2.3 The Shape Retrieval Contest
2.4 Evaluation Criteria in 3DOR
2.5 Summary
Part II: View Extraction, Selection, and Representation
Introduction
Chapter 3: View Extraction
Abstract
3.1 Introduction
3.2 Dense Sampling Viewpoints
3.3 Predefined Camera Array
3.4 Generated View
3.5 Summary
Chapter 4: View Selection
Abstract
4.1 Introduction
4.2 Unsupervised View Selection
4.3 Interactive View Selection
4.4 Summary
Chapter 5: View Representation
Abstract
5.1 Introduction
5.2 Shape Feature Extraction
5.3 The Bag-of-Visual-Features Method
5.4 Learning the Weights for Multiple Views
5.5 Summary
Part III: View-Based 3-D Object Comparison
Introduction
Chapter 6: Multiple-View Distance Metric
Abstract
6.1 Introduction
6.2 Fundamental Many-to-Many Distance Measures
6.3 Bipartite Graph Matching
6.4 Statistical Matching
6.5 Summary
Chapter 7: Learning-Based 3-D Object Retrieval
Abstract
7.1 Introduction
7.2 Learning Optimal Distance Metrics
7.3 3-D Object Relevance Estimation via Hypergraph Learning
7.4 Summary
Part IV: Conclusions and Future Work
Chapter 8: Conclusions and Future Work
Abstract
8.1 Summary of This Book
8.2 Future Work
No. of pages: 154
Language: English
Published: December 4, 2014
Imprint: Morgan Kaufmann
Paperback ISBN: 9780128024195
eBook ISBN: 9780128026236
YG
Yue Gao
Yue Gao is with the Department of Automation, Tsinghua University. His recent research focuses on the areas of neuroimaging, multimedia and remote sensing. He is a senior member of IEEE.
Affiliations and expertise
Department of Automation, Tsinghua University, Beijing, China
QD
Qionghai Dai
Qionghai Dai is with the Deparment of Automation, Tsinghua University. He has published more than 120 conference and journal papers, and holds 67 patents. His current research interests include the areas of computational photography, computational optical sensing, and compressed sensing imaging and vision. His work is motivated by challenging applications in the fields of computer vision, computer graphics, and robotics. He is a senior member of IEEE.
Affiliations and expertise
Deparment of Automation, Tsinghua University, Beijing, China