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010			_a 2023053648
020			_a9789811286490 _q(ebook)
020			_a9811286493 _q(ebook)
020			_z9789811286483 _q(hbk.)
040			_aWSPC _beng _cWSPC
050		4	_aQ325.73
072		7	_aCOM _x016000 _2bisacsh
072		7	_aCOM _x094000 _2bisacsh
082	0	4	_a006.31 _223
049			_aMAIN
245	0	0	_aDeep learning for 3D vision : _balgorithms and applications / _cedited by Xiaoli Li, Xulei Yang, Hao Su.
260			_aSingapore : _bWorld Scientific, _cc2024.
300			_a1 online resource (xii, 480 p.) : _bill. (some col.)
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction 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.
520			_a"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"-- _cPublisher's website.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
650		0	_aDeep learning (Machine learning)
650		0	_aComputer vision.
650		0	_aThree-dimensional imaging _xData processing.
655		0	_aElectronic books.
700	1		_aLi, Xiao-Li, _d1969-
700	1		_aYang, Xulei.
700	1		_aSu, Hao.
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/13683#t=toc
942			_cE-BOOK
999			_c49765 _d49765