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
Part I The Start 1. Introduction 2. The Benchmark and Evaluation
Part II View Extraction, Selection, and Representation 3. View Extraction 4. View Selection 5. View Representation
Part III View-Based 3-D Object Comparison 6. Multiple-View Distance Metric 7. Learning-based 3-D Object Retrieval
Part IV Conclusion 8. Conclusions and Future Work
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.
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.