View-based 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

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