Deep Learning for 3D Vision: Algorithms and Applications

ISBN 10: 9811286485 / ISBN 13: 9789811286483

Verlag: World Scientific Publishing Company, 2024

Beschreibung

Bestandsnummer des Verkäufers I-9789811286483

Bibliografische Details

• Titel: Deep Learning for 3D Vision: Algorithms and Applications
• Verlag: World Scientific Publishing Company
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 9811286485
• ISBN-13: 9789811286483
• Einband: Hardcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.