3d Point Cloud Analysis
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3d Point Cloud Analysis
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Author : Shan Liu
language : en
Publisher: Springer Nature
Release Date : 2021-12-10
3d Point Cloud Analysis written by Shan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-10 with Computers categories.
This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.
3d Point Cloud Analysis
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Author : Shan Liu
language : en
Publisher:
Release Date : 2021
3d Point Cloud Analysis written by Shan Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.
Reconstruction And Analysis Of 3d Scenes
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Author : Martin Weinmann
language : en
Publisher: Springer
Release Date : 2016-03-17
Reconstruction And Analysis Of 3d Scenes written by Martin Weinmann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-17 with Computers categories.
This unique work presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced, incorporating each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: reviews techniques for the acquisition of 3D point cloud data and for point quality assessment; explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data; proposes an original approach to keypoint-based point cloud registration; discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping; presents a novel framework for 3D scene analysis.
Deep Learning For 3d Point Clouds
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Author : Wei Gao
language : en
Publisher: Springer Nature
Release Date : 2024-12-06
Deep Learning For 3d Point Clouds written by Wei Gao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-06 with Computers categories.
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.
Point Cloud Processing For Environmental Analysis In Autonomous Driving Using Deep Learning
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Author : Martin Simon
language : en
Publisher: BoD – Books on Demand
Release Date : 2023-01-01
Point Cloud Processing For Environmental Analysis In Autonomous Driving Using Deep Learning written by Martin Simon and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Computers categories.
Autonomous self-driving cars need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types, such as lidar scanners, are in use. This thesis contributes highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds originating from lidar sensors. First, a single-shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and joint probabilistic tracking to stabilize predictions and filter outliers. The last part presents an evaluation of data from automotive-grade lidar scanners. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation.
Neural Information Processing
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Author : Mufti Mahmud
language : en
Publisher: Springer Nature
Release Date : 2025-06-07
Neural Information Processing written by Mufti Mahmud and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-07 with Computers categories.
The eleven-volume set LNCS 15286-15296 constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 318 regular papers presented in the proceedings set were carefully reviewed and selected from 1301 submissions. They focus on four main areas, namely: theory and algorithms; cognitive neurosciences; human-centered computing; and applications.
Recent Advances In 3d Geoinformation Science
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Author : Thomas H. Kolbe
language : en
Publisher: Springer Nature
Release Date : 2024-02-20
Recent Advances In 3d Geoinformation Science written by Thomas H. Kolbe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-20 with Technology & Engineering categories.
The book includes the contributions to the international conference “18th 3D GeoInfo”. The papers published in the book were selected through a double-blind review process. 3D GeoInfo has been the forum joining researchers, professionals, software developers, and data providers designing and developing innovative concepts, tools, and application related to 3D geo data processing, modeling, management, analytics, and simulation. A big focus is on topics related to data modeling for 3D city and landscape models as well as their many and diverse applications. This conference series is very successfully running since 2006 and has been hosted by countries in Europe, Asia, Africa, North America, and Australia. In the period 2006 to 2017, the proceedings has been published by Springer in this series with Thomas H. Kolbe being the editor of the 2010 edition of the conference proceedings. 18th 3DGeoInfo was organized by Technical University of Munich in cooperation with the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF), the local associations Runder Tisch GIS e.V. (Round Table GIS) and Leonhard Obermeyer Center—TUM Center of Digital Methods for the Built Environment, and the City of Munich. The international program committee consisted of committee members of previous 3D GeoInfo conferences and further leading scientists in the field of 3D Geoinformation Science.
Pattern Recognition And Computer Vision
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Author : Huimin Ma
language : en
Publisher: Springer Nature
Release Date : 2021-10-22
Pattern Recognition And Computer Vision written by Huimin Ma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-22 with Computers categories.
The 4-volume set LNCS 13019, 13020, 13021 and 13022 constitutes the refereed proceedings of the 4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021, held in Beijing, China, in October-November 2021. The 201 full papers presented were carefully reviewed and selected from 513 submissions. The papers have been organized in the following topical sections: Object Detection, Tracking and Recognition; Computer Vision, Theories and Applications, Multimedia Processing and Analysis; Low-level Vision and Image Processing; Biomedical Image Processing and Analysis; Machine Learning, Neural Network and Deep Learning, and New Advances in Visual Perception and Understanding.
Pattern Recognition
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Author : Apostolos Antonacopoulos
language : en
Publisher: Springer Nature
Release Date : 2024-12-03
Pattern Recognition written by Apostolos Antonacopoulos and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1–5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics.
Gis And Spatial Analysis
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Author : Jorge Rocha
language : en
Publisher: BoD – Books on Demand
Release Date : 2023-07-12
Gis And Spatial Analysis written by Jorge Rocha and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-12 with Science categories.
The objective of spatial analysis techniques is to describe the patterns existing in spatial data and to establish, preferably quantitatively, the relationships between different geographic variables. The notion of spatial analysis in a Geographic Information Systems (GIS) environment encompasses the idea of integrating spatial data and alphanumeric attributes and translating it into a series of functions related to selection and data search, on the one hand, and with modeling, on the other. There have been substantial advances in spatial analysis techniques in GIS, mainly in the form of more faithfully apprehending the relationships inherent to the geographic phenomenon, something that was proven impossible to do with non-spatial techniques. Nowadays, spatial analysis involves a set of techniques used to analyze and model variables with distribution in space and/or time. The new era of spatial analysis must also consider the possibilities of integrating artificial intelligence in simulation (geosimulation) processes in computerized environments (geocomputation) in close relationship with models developed in real situations. GIS have emerged as useful tools in geographic modeling processes, helping to answer questions about the time variability of the landscape structure, study the behavior of fire, predict areas of urban expansion, analyze propagation phenomena, model animal movement and behavior, and determine periods and areas of high risk of flooding, among other phenomena. GIS and Spatial Analysis is a critical book that provides different methodologies that combine the potential data (including Big Data) analysis with GIS applications. It gives readers a comprehensive overview of the current state-of-the-art methods of spatial analysis, focusing both on the new philosophical and theoretical foundations for spatial analysis and on a flexible framework for analysis in the real world, for problems such as complexity and uncertainty.