3D Shape Analysis

HAMID LAGA, PHD, is an Associate Professor and Head of the Information Technology discipline in the School of Engineering and IT, Murdoch University, Australia. He is also Adjunct Associate Professor at the Phenomics and Bioinformatics Research Centre, University of South Australia, Australia.YULAN GUO, PHD, is an Assistant Professor in the College of Electronic Science, National University of Defense Technology (NUDT), China. He is also a Research Fellow at the Institute of Computing Technology, Chinese Academy of Sciences, China.HEDI TABIA, PHD, is an Associate Professor at École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), France.ROBERT B. FISHER, PHD, is a Professor at The University of Edinburgh, United Kingdom, where he was previously Dean of Research in the College of Science and Engineering.MOHAMMED BENNAMOUN, PHD, is a Winthrop Professor in the School of Computer Science and Software Engineering at The University of Western Australia, Australia.

• Preface xvAcknowledgments xvii1 Introduction 11.1 Motivation 11.2 The 3D Shape Analysis Problem 21.3 About This Book 51.4 Notation 9Part I Foundations 112 Basic Elements of 3D Geometry and Topology 132.1 Elements of Differential Geometry 132.2 Shape, Shape Transformations, and Deformations 302.3 Summary and Further Reading 383 3D Acquisition and Preprocessing 413.1 Introduction 413.2 3D Acquisition 413.3 Preprocessing 3D Models 563.4 Summary and Further Reading 62Part II 3D Shape Descriptors 654 Global Shape Descriptors 674.1 Introduction 674.2 Distribution-Based Descriptors 694.3 View-Based 3D Shape Descriptors 734.4 Spherical Function-Based Descriptors 774.5 Deep Neural Network-Based 3D Descriptors 834.6 Summary and Further Reading 895 Local Shape Descriptors 935.1 Introduction 935.2 Challenges and Criteria 945.3 3D Keypoint Detection 965.4 Local Feature Description 1135.5 Feature Aggregation Using Bag of Feature Techniques 1265.6 Summary and Further Reading 131Part III 3D Correspondence and Registration 1356 Rigid Registration 1376.1 Introduction 1376.2 Coarse Registration 1386.3 Fine Registration 1526.4 Summary and Further Reading 1607 Nonrigid Registration 1617.1 Introduction 1617.2 Problem Formulation 1627.3 Mathematical Tools 1657.4 Isometric Correspondence and Registration 1687.5 Nonisometric (Elastic) Correspondence and Registration 1717.6 Summary and Further Reading 1848 Semantic Correspondences 1878.1 Introduction 1878.2 Mathematical Formulation 1888.3 Graph Representation 1918.4 Energy Functions for Semantic Labeling 1948.5 Semantic Labeling 1968.6 Examples 2028.7 Summary and Further Reading 204Part IV Applications 2079 Examples of 3D Semantic Applications 2099.1 Introduction 2099.2 Semantics: Shape or Status 2099.3 Semantics: Class or Identity 2129.4 Semantics: Behavior 2169.5 Semantics: Position 2199.6 Summary and Further Reading 22110 3D Face Recognition 22310.1 Introduction 22310.2 3D Face Recognition Tasks, Challenges and Datasets 22410.3 3D Face Recognition Methods 22810.4 Summary 23911 Object Recognition in 3D Scenes 24111.1 Introduction 24111.2 Surface Registration-Based Object Recognition Methods 24111.3 Machine Learning-Based Object Recognition Methods 25511.4 Summary and Further Reading 26512 3D Shape Retrieval 26712.1 Introduction 26712.2 Benchmarks and Evaluation Criteria 27012.3 Similarity Measures 27512.4 3D Shape Retrieval Algorithms 28012.5 Summary and Further Reading 28413 Cross-domain Retrieval 28513.1 Introduction 28513.2 Challenges and Datasets 28713.3 Siamese Network for Cross-domain Retrieval 29013.4 3D Shape-centric Deep CNN 29213.5 Summary and Further Reading 30014 Conclusions and Perspectives 301References 303Index 337