Deep learning for 3D vision : algorithms and applications /
Deep learning for 3D vision : algorithms and applications /
edited by Xiaoli Li, Xulei Yang, Hao Su.
- Singapore : World Scientific, c2024.
- 1 online resource (xii, 480 p.) : ill. (some col.)
Includes bibliographical references and index.
Introduction to 3D deep learning -- Masked autoencoders for 3D point cloud self-supervised learning -- You only need one thing one click: self-training for weakly supervised 3D scene understanding -- Representation learning for dynamic 3D scenes -- eDiGS: extended divergence-guided shape implicit neural representation for unoriented point clouds -- Improving monocular 3D object detection by synthetic images with virtual depth -- Robust structured declarative classifiers for point clouds -- Towards inference stage robust 3D point cloud recognition -- Algorithm-system-hardware co-design for efficient 3D deep learning -- Sampling strategies for efficient segmentation and object detection of 3D point clouds -- Efficient 3D representation learning for medical image analysis -- AI-based 3D metrology and defect detection of HBMs in XRM scans.
"3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications. This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing. This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning"--
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
9789811286490 9811286493
2023053648
Deep learning (Machine learning)
Computer vision.
Three-dimensional imaging--Data processing.
Electronic books.
Q325.73
006.31
Includes bibliographical references and index.
Introduction to 3D deep learning -- Masked autoencoders for 3D point cloud self-supervised learning -- You only need one thing one click: self-training for weakly supervised 3D scene understanding -- Representation learning for dynamic 3D scenes -- eDiGS: extended divergence-guided shape implicit neural representation for unoriented point clouds -- Improving monocular 3D object detection by synthetic images with virtual depth -- Robust structured declarative classifiers for point clouds -- Towards inference stage robust 3D point cloud recognition -- Algorithm-system-hardware co-design for efficient 3D deep learning -- Sampling strategies for efficient segmentation and object detection of 3D point clouds -- Efficient 3D representation learning for medical image analysis -- AI-based 3D metrology and defect detection of HBMs in XRM scans.
"3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications. This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing. This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning"--
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
9789811286490 9811286493
2023053648
Deep learning (Machine learning)
Computer vision.
Three-dimensional imaging--Data processing.
Electronic books.
Q325.73
006.31