TY  - BOOK
AU  - Li,Xiao-Li
AU  - Yang,Xulei
AU  - Su,Hao
TI  - Deep learning for 3D vision: algorithms and applications 
SN  - 9789811286490
AV  - Q325.73 
U1  - 006.31 23
PY  - 2024///
CY  - Singapore
PB  - World Scientific
KW  - Deep learning (Machine learning)
KW  - Computer vision
KW  - Three-dimensional imaging
KW  - Data processing
KW  - Electronic books
N1  - 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
N2  - "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"--
UR  - https://www.worldscientific.com/worldscibooks/10.1142/13683#t=toc
ER  -