MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03839nam a2200421 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
WSP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260416153413.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
240314s2024 si a ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789811286490 |
| -- |
(ebook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9811286493 |
| -- |
(ebook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Cancelled/invalid ISBN |
9789811286483 |
| -- |
(hbk.) |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM |
| Subject category code subdivision |
016000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM |
| Subject category code subdivision |
094000 |
| Source |
bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
23 |
| 049 ## - LOCAL HOLDINGS (OCLC) |
|---|
| Holding library |
MAIN |
| 245 00 - TITLE STATEMENT |
|---|
| Title |
Deep learning for 3D vision : |
| Remainder of title |
algorithms and applications / |
| Statement of responsibility, etc |
edited by Xiaoli Li, Xulei Yang, Hao Su. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Singapore : |
| Name of publisher, distributor, etc |
World Scientific, |
| Date of publication, distribution, etc |
c2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource (xii, 480 p.) : |
| Other physical details |
ill. (some col.) |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| 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. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"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"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Deep learning (Machine learning) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer vision. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Three-dimensional imaging |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Li, Xiao-Li, |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Yang, Xulei. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Su, Hao. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://www.worldscientific.com/worldscibooks/10.1142/13683#t=toc">https://www.worldscientific.com/worldscibooks/10.1142/13683#t=toc</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Electronic Books |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| -- |
Publisher's website. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
System requirements: Adobe Acrobat Reader. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| General subdivision |
Data processing. |
| 655 #0 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Dates associated with a name |
1969- |
