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. Contents: Introduction to 3D Deep Learning (Xiaoli Li, Xulei Yang, and Hao Su) Masked Autoencoders for 3D Point Cloud Self-Supervised Learning (Yatian Pang, Zhenghua Chen, and Li Yuan) You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding (Zhengzhe Liu, Xiaojuan Qi, and Chi-Wing Fu) Representation Learning for Dynamic 3D Scenes (Yunzhu Li and Jiajun Wu) eDiGS: Extended Divergence-Guided Shape Implicit Neural Representation for Unoriented Point Clouds (Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, and Stephen Gould) Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth (Chenhang He and Lei Zhang) Robust Structured Declarative Classifiers for Point Clouds (Ziming Zhang, Kaidong Li, and Guanghui Wang) Towards Inference Stage Robust 3D Point Cloud Recognition (Yongyi Su, Xun Xu, and Kui Jia) Algorithm-System-Hardware Co-Design for Efficient 3D Deep Learning (Zhijian Liu, Haotian Tang, Yujun Lin, and Song Han) Sampling Strategies for Efficient Segmentation and Object Detection of 3D Point Clouds (Qingyong Hu) Efficient 3D Representation Learning for Medical Image Analysis (Yucheng Tang, Jie Liu, Zongwei Zhou, Xin Yu, and Yuankai Huo) AI-Based 3D Metrology and Defect Detection of HBMs in XRM Scans (Ramanpreet Singh Pahwa, Richard Chang, and Wang Jie) Readership: Advanced undergraduate and graduate students, researchers and practitioners in the fields of 3D computer vision, robot perception and autonomous driving. Key Features: 3D deep learning is a rapidly developing field with tremendous research value and potential real-world applications This is the first book collating the most recent research advances on 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges occurred in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications 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 This book will not only contribute to the advancement of 3D deep learning, but also inspire further research and create more real-world impact in this exciting field
