Hao Su – författare

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.

This book explores peg-in-hole assembly strategies to study robotic intelligent assembly. It presents several state-of-the-art principles for peg-in-hole assembly strategies, supported by experimental evidence. In pursuit of theoretical innovation, the author summarizes their research on robotic intelligent assembly over the past decade, highlighting the limitations of model-based algorithms in complex assembly environments and the importance of data efficiency for learning-based algorithms. Each algorithm is supported by extensive experimentation and results demonstrating its effectiveness. A review of research ideas provides readers with a comprehensive understanding of the progress made in this field. This monograph is intended for undergraduate and postgraduate students interested in robotic intelligent assembly, researchers studying robotic intelligent assembly algorithms, and electronic, mechanical, and computer engineers engaged in industrial robot-assisted assembly.

This book explores peg-in-hole assembly strategies to study robotic intelligent assembly. It presents several state-of-the-art principles for peg-in-hole assembly strategies, supported by experimental evidence. In pursuit of theoretical innovation, the author summarizes their research on robotic intelligent assembly over the past decade, highlighting the limitations of model-based algorithms in complex assembly environments and the importance of data efficiency for learning-based algorithms. Each algorithm is supported by extensive experimentation and results demonstrating its effectiveness. A review of research ideas provides readers with a comprehensive understanding of the progress made in this field. This monograph is intended for undergraduate and postgraduate students interested in robotic intelligent assembly, researchers studying robotic intelligent assembly algorithms, and electronic, mechanical, and computer engineers engaged in industrial robot-assisted assembly.