View-based 3-D Object Retrieval
- 1st Edition - December 4, 2014
- Authors: Yue Gao, Qionghai Dai
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 2 4 1 9 - 5
- eBook ISBN: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 mu… Read more
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Request a sales quoteContent-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
- Chapter 8: Conclusions and Future Work
- No. of pages: 154
- Language: English
- Edition: 1
- Published: December 4, 2014
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780128024195
- eBook ISBN: 9780128026236
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Yue Gao
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