DEEP LEARNING FOR 3D VISION | ALGORITHMS AND APPLICATIONS

Li Xiaoli

ISBN 10: 9811286485 / ISBN 13: 9789811286483

Published by World Scientific, 2024

About this Item

DEEP LEARNING FOR 3D VISION | ALGORITHMS AND APPLICATIONS | Li Xiaoli | Buch | Englisch | 2024 | World Scientific | EAN 9789811286483 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 128155377

Bibliographic Details

• Title: DEEP LEARNING FOR 3D VISION | ALGORITHMS AND APPLICATIONS
• Publisher: World Scientific
• Publication Date: 2024
• Language: English
• ISBN 10: 9811286485
• ISBN 13: 9789811286483
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 157H x 31D millimeters 235 millimeters width by 157 millimeters height by 31 millimeters depth
• Weight: 859 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.