Shan Liu - 3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
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- Book:3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods: summary, description and annotation
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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.
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This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Dedicated to my son, William
Shan Liu
Dedicated to my parents and grandparents for their love and support
Min Zhang
Dedicated to my parents for their love, encouragement, and support
Pranav Kadam
Dedicated to my wife, Terri, and my daughter, Allison
C.-C. Jay Kuo
Three-dimensional (3D) point clouds are gaining increasing attention for the emerging applications of 3D vision. Point clouds have widespread use in several spectrums of fields, include robotics, 3D graphics, autonomous driving, virtual reality, and so on. To keep pace with the increasing applications, the research and development of methods and algorithms to effectively store, process, and infer meaning from point cloud is on the rise. The traditional algorithms for analyzing point clouds focus on encoding the local geometric properties of points. The success of deep learning methods for processing image data led to similar networks being developed for point clouds. Present day research heavily involves the development of deep networks for various point cloud processing tasks.
The aim of this book is to give a high-level overview of point clouds and acquaint the reader with some of the most popular methods and techniques for point cloud processing. The ideal audience are those with a basic knowledge of linear algebra, machine learning, and deep learning algorithms, who wish to explore point clouds in their career or as a hobby.
This book is organized into five chapters. Chapter ) includes a summary and some concluding remarks as well as possible future research directions.
received the B.Eng. degree in Electronic Engineering from the Tsinghua University and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California. She is currently a Distinguished Scientist at the Tencent and General Manager of the Tencent Media Lab. She was the former Director of Media Technology Division at MediaTek, USA. She was also formerly with the MERL and Sony, etc. Dr. Liu has been actively contributing to international standards for more than a decade. She has numerous technical proposals adopted into various standards, such as H.266/VVC, H.265/HEVC, OMAF, DASH, MMT, and PCC, and served as co-editor of H.265/HEVC SCC and H.266/VVC. Meanwhile, technologies and products developed by her and her team have served hundreds of millions of users. Dr. Liu holds more than 200 granted patents and has published more than 100 technical papers. She was named APSIPA Industrial Distinguished Leader by the Asia-Pacific Signal and Information Processing Association in 2018, and 50 Women in Tech by the Forbes China in 2020. She is on the Editorial Board of IEEE Transactions on Circuits and Systems for Video Technology (2018present) and received the Best AE Award in 2019 and 2020. Her research interests include audio-visual, volumetric, immersive, and emerging media compression, intelligence, transport, and systems.
received her B.E. degree from the School of Science, Nanjing University of Science and Technology, Nanjing, China, and her M.S. degree from the Viterbi School of Engineering, University of Southern California (USC), Los Angeles, USA, in 2017 and 2019, respectively. She joined the Media Communications Laboratory (MCL) in the summer of 2018 and is currently a Ph.D. student in the USC, guided by Prof. C.-C. Jay Kuo. Her research interests include point cloud processing and analysis-related problems, such as point cloud classification, registration, and segmentation and detection, in the field of 3D computer vision, machine learning, and perception.
received his M.S. degree in Electrical Engineering from the University of Southern California, Los Angeles, USA, in 2020, and the Bachelors degree in Electronics and Telecommunication Engineering from the Savitribai Phule Pune University, Pune, India, in 2018. He is currently pursuing the Ph.D. degree in Electrical Engineering from the University of Southern California. He is actively involved in the research and development of methods for point cloud analysis and processing. His research interests include 3D computer vision, machine learning, and perception.
received the Ph.D. degree in Electrical Engineering from the Massachusetts Institute of Technology, Cambridge, in 1987. He is currently the holder of William M. Hogue Professorship, a Distinguished Professor of Electrical and Computer Engineering and Computer Science, and the Director of the USC Multimedia Communications Laboratory (MCL) at the University of Southern California. Dr. Kuo is a Fellow of the American Association for the Advancement of Science (AAAS), the Institute of Electrical and Electronics Engineers (IEEE), the National Academy of Inventors (NAI), and the International Society for Optical Engineers (SPIE). He has received several awards for his research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 20102011 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 2020 IEEE TCMC Impact Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award.
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