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Total Size:
24.9 MB
Info Hash:
AD5AB6C26A20BA45B3E7E7E6D3BDE7D97785C086
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Added:
April 22, 2026, 1:35 p.m.
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(Last updated: April 22, 2026, 1:38 p.m.)
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| Wei G. Deep Learning for 3D Point Clouds 2025.pdf | 24.9 MB |
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| Uploaded by andryold1 | Size 26.7 MB | Health [ 61 /33 ] | Added 2025-08-21 |
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| Uploaded by pOrnMaN | Size 650.8 MB | Health [ 152 /9 ] | Added 2025-07-22 |
NOTE
SOURCE: Wei G. Deep Learning for 3D Point Clouds 2025
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COVER
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MEDIAINFO
Textbook in PDF format As an efficient 3D vision solution, point clouds have been widely applied into diverse engineering scenarios, including immersive media communication, autonomous driving, reverse engineering, robots, topography mapping, digital twin city, medical analysis, digital museum, etc. Thanks to the great developments of Deep Learning theories and methods, 3D point cloud technologies have undergone fast growth during the past few years, including diverse processing and understanding tasks. Human and machine perception can be benefited from the success of using Deep Learning approaches, which can significantly improve 3D perception modeling and optimization, as well as 3D pre-trained and large models. This book delves into these research frontiers of Deep Learning-based point cloud technologies. The subject of this book focuses on diverse intelligent processing technologies for the fast-growing 3D point cloud applications, especially using Deep Learning-based approaches. The Deep Learning-based enhancement and analysis methods are elaborated in detail, as well as the pre-trained and large models with 3D point clouds. This book carefully presents and discusses the newest progresses in the field of Deep Learning-based point cloud technologies, including basic concepts, fundamental background knowledge, enhancement, analysis, 3D pre-trained and large models, multi-modal learning, open source projects, engineering applications, and future prospects. Readers can systematically learn the knowledge and the latest developments in the field of Deep Learning-based point cloud technologies. This book provides vivid illustrations and examples, and the intelligent processing methods for 3D point clouds. Readers can be equipped with an in-depth understanding of the latest advancements of this rapidly developing research field. This book puts an emphasis on the perspectives of Deep Learning, 3D human and machine perception, and large models. The detailed chapters are organized as follows: Chapter 1 presents an overview of the 3D world representation with point clouds, including representative datasets, processing tasks, and applications. Chapter 2 introduces the fundamental background knowledge of deep learning, and several basic deep neural networks for point cloud tasks. Chapters 3 and 4 demonstrate the deep learning-based point cloud enhancement principles and methods, including upsampling, downsampling, frame interpolation, completion, and denoising. Chapters 5 and 6 delve into the deep learning-based point cloud analysis principles and methods, including classification and segmentation, object detection, tracking, retrieval, registration, and multimodal analysis. Chapter 7 illustrates the point cloud pre-trained models and large models, including the fundamental principles, and point cloud-based pre-trained models and large models. Chapter 8 presents the point cloud-language multi-modal learning methods, including large language modeling in natural language processing, 2D vision-language models, 2D vision-language multi-modal large language models, 3D point cloud multi-modal large language models, and 3D embodied intelligence. Chapter 9 outlines the point cloud open source projects. This chapter starts with an introduction to the open source culture and community, and then presents the open source works in two aspects, including point cloud processing and analysis algorithms. Chapter 10 discusses the typical engineering applications of point cloud technologies, which introduces and analyzes the application status quo of point cloud technologies in autonomous driving, reverse engineering, robotics, topographic mapping, digital twin cities, medical analysis, digital museum, etc. Chapter 11 concludes the future works for various point cloud technologies, including deep learning-based enhancement, deep learning-based analysis, large models, open source projects, and the point cloud applications. This book presents the fundamental knowledge and recent advances in Deep Learning-based point cloud technologies. As a textbook on 3D point cloud compression technologies, this book comprises the above selected chapters. Through the progressive presentation, readers can comprehensively understand and master the basic knowledge, the main techniques, and the development trends in Deep Learning-based point cloud processing tasks
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