| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
00000nam a22000001i 4500 |
| 001 |
in00005657821 |
| 006 |
m |o d | |
| 007 |
cr |n||||||||| |
| 008 |
250624s2024 njua ob 001 0 eng d |
| 005 |
20250810205606.2 |
| 035 |
|
|
|a (OCoLC)1455531455
|
| 035 |
|
|
|a (OCoLC)on1455531455
|
| 040 |
|
|
|a NhCcYBP
|b eng
|e rda
|e pn
|c NhCcYBP
|
| 020 |
|
|
|a 9789811286490 (electronic bk.)
|
| 020 |
|
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|a 9811286493 (electronic bk.)
|
| 020 |
|
|
|z 9789811286483
|
| 020 |
|
|
|z 9811286485
|
| 050 |
|
4 |
|a TA1560
|b .D44 2024
|
| 082 |
0 |
4 |
|a 006.3/1
|2 23/eng/20240920
|
| 245 |
0 |
0 |
|a Deep learning for 3D vision :
|b algorithms and applications /
|c edited by Xiaoli Li, A*STAR, Singapore, Xulei Yang, A*STAR, Singapore, Hao Su, UC San Diego, USA.
|
| 246 |
3 |
0 |
|a Deep learning for 3 dimensional vision
|
| 246 |
3 |
|
|a Deep learning for three dimensional vision
|
| 264 |
|
1 |
|a New Jersey :
|b World Scientific,
|c [2024]
|
| 300 |
|
|
|a xii, 480 pages :
|b illustrations (some color.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 504 |
|
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|a Includes bibliographical references and index.
|
| 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"--
|c Provided by publisher.
|
| 588 |
|
|
|a Description based on print version record.
|
| 650 |
|
0 |
|a Three-dimensional imaging
|x Data processing.
|
| 650 |
|
0 |
|a Deep learning (Machine learning)
|
| 650 |
|
0 |
|a Computer vision.
|
| 650 |
|
6 |
|a Apprentissage profond.
|0 (CaQQLa)000311746
|
| 650 |
|
6 |
|a Vision par ordinateur.
|0 (CaQQLa)201-0074889
|
| 650 |
|
6 |
|a Imagerie tridimensionnelle
|0 (CaQQLa)201-0269004
|x Informatique.
|0 (CaQQLa)201-0380011
|
| 700 |
1 |
|
|a Li, Xiao-Li,
|d 1969-
|e editor.
|
| 700 |
1 |
|
|a Yang, Xulei,
|e editor.
|
| 700 |
1 |
|
|a Su, Hao,
|e editor.
|
| 776 |
0 |
8 |
|c Original
|z 9789811286483
|z 9811286485
|w (DLC) 2023053648
|w (OCoLC)1426008200
|
| 852 |
8 |
|
|b POD
|z This title is available for the library to purchase for your use. Click the "Purchase It For Me" button to place a request. This item will take 5-10 business days to arrive.
|
| 955 |
|
|
|a Ebook POD title
|
| 961 |
|
|
|m 565731
|
| 999 |
f |
f |
|s e3d9c21e-9dac-4e35-84ee-b13ec066886f
|i 5099755a-8308-4724-8ba7-c35dc251e107
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Sterling C. Evans Library
|d Purchase on Demand
|t 0
|e TA1560 .D44 2024
|h Library of Congress classification
|
| 998 |
f |
f |
|a TA1560 .D44 2024
|t 0
|l Purchase on Demand
|