Deep Learning for 3D Vision: Algorithms and ApplicationsКНИГИ » ПРОГРАММИНГ
Название: Deep Learning for 3D Vision: Algorithms and Applications Автор: Xiaoli Li, Xulei Yang, Hao Su Издательство: World Scientific Publishing Год: 2024 Страниц: 493 Язык: английский Формат: pdf (true) Размер: 20.9 MB
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 ?rst 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 scienti?c simulations, and many more.
Deep Learning is a sub?eld of Machine Learning that utilises 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 signi?cant 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 signi?cant progress in tasks, such as 3D object detection and segmentation, point cloud classi?cation, 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 Learningg 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.
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Deep Learning for NLP and Speech Recognition Название: Deep Learning for NLP and Speech Recognition Автор: Uday Kamath, John Liu, James Whitaker Издательство: Springer Год: 2019 Страниц: 640 ...
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Deep Learning for 3D Vision: Algorithms and Applications 
