"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"-- Provided by publisher


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    Titel :

    Deep learning for 3d vision : algorithms and applications


    Beteiligte:
    Li, Xiao-Li (Herausgeber:in) / Yang, Xulei (Herausgeber:in) / Su, Hao (Herausgeber:in)

    Erscheinungsdatum :

    2024


    Format / Umfang :

    1 Online-Ressource (xii, 480 Seiten)


    Anmerkungen:

    Illustrationen
    Campusweiter Zugriff (Universität Hannover) - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.




    Medientyp :

    Buch


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :