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li x deep learning 3d vision algorithms applications 2024

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Textbook in PDF format 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. For any AI-enabled agent to accomplish its task, visual understanding or perception is the ferst step towards interacting with the three-dimensional (3D) world. Due to its inherent limitations, visual understanding techniques based solely on two-dimensional (2D) images may be inadequate for real-world applications. This calls for 3D deep learning techniques that operate on 3D data, which enables a direct visual understanding of the 3D world. In recent years, 3D Deep Learning has been attracting increasing attention. As we live in a 3D world, 3D Deep Learning is a natural way to perceive and understand our environment, enabling emerging and new industrial applications, such as autonomous driving, robot perception, medical imaging, and scientific simulations, and many more. Deep Learning is a subfield of Machine Learning that utilis Artificial Neural Networks to learn from large amounts of data. In Deep Learning, neural networks are composed of multiple layers of interconnected nodes, or neurons, that process and transform data, allowing the network to automatically learn complex features and patterns in the data. Deep Learning has seen significant advancements in recent years, driven by both the availability of large datasets and advances in computing power and hardware. This has led to the development of increasingly complex models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more recently, transformer models, such as the Generative Pre-trained Transformer (GPT) family. In the context of 3D Deep Learning, deep neural networks have been adapted and extended to work with 3D data, including point clouds, meshes, and volumetric data. This has led to significant progress in tasks, such as 3D object detection and segmentation, point cloud classification, and 3D reconstruction. Nevertheless, working with 3D data presents unique challenges compared to 2D data, such as sparsity, irregularity, and complexity of the geometric structure. Therefore, new methods and architectures are needed to tackle these challenges and unlock the potential of 3D Deep Learning for a wide range of 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. Preface Introduction to 3D Deep Learning Masked Autoencoders for 3D Point Cloud Self-Supervised Learning You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding Representation Learning for Dynamic 3D Scenes eDiGS: Extended Divergence-Guided Shape Implicit Neural Representation for Unoriented Point Clouds Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth Robust Structured Declarative Classifiers for Point Clouds Towards Inference Stage Robust 3D Point Cloud Recognition Algorithm-System-Hardware Co-design for Efficient 3D Deep Learning Sampling Strategies for Efficient Segmentation and Object Detection of 3D Point Clouds Efficient 3D Representation Learning for Medical Image Analysis AI-Based 3D Metrology and Defect Detection of HBMs in XRM Scans

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li x deep learning 3d vision algorithms applications 2024

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