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Deep learning for 3D vision : algorithms and applications / edited by Xiaoli Li, Xulei Yang, Hao Su.

Contributor(s): Material type: TextPublication details: Singapore : World Scientific, c2024.Description: 1 online resource (xii, 480 p.) : ill. (some col.)ISBN:
  • 9789811286490
  • 9811286493
Subject(s): Genre/Form: DDC classification:
  • 006.31 23
LOC classification:
  • Q325.73
Online resources:
Contents:
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.
Summary: "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"-- Publisher's website.
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Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Electronic Books CUTN Central Library 006.31 (Browse shelf(Opens below)) Link to resource Available EB04949

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"-- Publisher's website.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

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