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Overview: 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 first 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. 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.
Genre: Non-Fiction > Tech & Devices
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