3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
暫譯: 3D 點雲分析：傳統方法、深度學習與可解釋機器學習方法
Liu, Shan, Zhang, Min, Kadam, Pranav
- 出版商: Springer
- 出版日期: 2022-12-11
- 售價: $5,260
- 貴賓價: 9.5 折 $4,997
- 語言: 英文
- 頁數: 146
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3030891828
- ISBN-13: 9783030891824
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相關分類:
Machine Learning、DeepLearning
海外代購書籍(需單獨結帳)
商品描述
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.
商品描述(中文翻譯)
這本書介紹了點雲及其在工業中的應用，以及最常用的數據集。它主要集中於三個計算機視覺任務——點雲分類、分割和配準——這些都是任何基於點雲的系統的基礎。傳統點雲處理方法的概述幫助讀者快速建立背景知識，而點雲的深度學習方法則包括對過去幾年突破性進展的全面分析。全新的可解釋機器學習方法專為點雲學習設計，這些方法輕量且易於訓練，隨後將進行詳細介紹。提供了定量和定性性能評估。對三種類型方法的比較和分析幫助讀者更深入地理解。
隨著2D視覺領域豐富的深度學習文獻，3D視覺研究者自然傾向於開發點雲處理的深度學習方法。自2017年以來，點雲的深度學習逐漸受到關注，該領域的會議論文數量持續增加。與2D圖像不同，點雲沒有特定的順序，這使得通過深度學習進行點雲處理相當具有挑戰性。此外，由於點雲的幾何特性，傳統方法在工業中仍然被廣泛使用。因此，本書旨在通過提供傳統方法和最先進的深度學習方法的綜合概述，使讀者熟悉這一領域。
本書的主要部分集中於可解釋的機器學習，作為深度學習的一種不同方法。可解釋的機器學習方法相較於傳統方法和深度學習方法提供了一系列優勢。這是本書的一個主要亮點和創新。通過使用我們的方法解決三個研究任務——3D物體識別、分割和配準——讀者將能夠感受到如何以不同的方式解決問題，並能將這些框架應用於其他3D計算機視覺任務，從而激發他們未來的研究靈感。
提供了大量實驗、分析和對三個3D計算機視覺任務（物體識別、分割、檢測和配準）的比較，以便讀者學習如何解決困難的計算機視覺問題。
作者簡介
Shan Liu received her B.Eng. degree in electronic engineering from Tsinghua University, and M.S. and Ph.D. degrees in electrical engineering from the University of Southern California, respectively. She is currently a Distinguished Scientist at Tencent and General Manager of Tencent Media Lab. She was formerly Director of Media Technology Division at MediaTek USA. She was also formerly with MERL and Sony, etc. Dr. Liu has been an active contributor 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, PCC, and served as an Editor of H.265/HEVC SCC and H.266/VVC standards. She is also heavily involved in multimedia technology productization and made instrumental contributions to several million-user products. Dr. Liu holds more than 200 granted patents and has published more than 100 technical papers. She was named "APSIPA Industrial Distinguished Leader" by Asia-Pacific Signal and Information Processing Association in 2018, and "50 Women in Tech" by Forbes China in 2020. She is on the Editorial Board of IEEE Transactions on Circuits and Systems for Video Technology (2018-present) and received the Best AE Award in 2019 and 2020, respectively. Her research interests include audio-visual, volumetric, immersive and emerging media compression, intelligence, transport and systems.
Min Zhang 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, US, in 2017 and 2019, respectively. She joined Media Communications Laboratory (MCL) in 2018 summer and is currently a Ph.D. student in USC, guided by Prof. C.-C. Jay Kuo. Her research interests include point cloud processing and analysis related problems, i.e., point cloud classification, registration, and segmentation and detection, in the field of 3D computer vision, machine learning, and perception.
Pranav Kadam received his MS degree in Electrical Engineering from the University of Southern California, Los Angeles, USA in 2020, and the Bachelor's degree in Electronics and Telecommunication Engineering from Savitribai Phule Pune University, Pune, India in 2018. He is currently pursuing the PhD degree in Electrical Engineering from the University of Southern California. He is actively involved in research and development of methods for point cloud analysis and processing. His research interests include 3D computer vision, machine learning, and perception.
C.-C. Jay Kuo 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 2010-11 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.
作者簡介(中文翻譯)
劉珊於清華大學獲得電子工程學士學位，並於南加州大學獲得電機工程碩士及博士學位。她目前是騰訊的傑出科學家及騰訊媒體實驗室的總經理。她曾擔任美國聯發科技媒體技術部的主任，並曾在MERL和Sony等公司工作。劉博士在國際標準方面活躍貢獻超過十年，提出了多項技術提案被納入各種標準，如H.266/VVC、H.265/HEVC、OMAF、DASH、MMT、PCC，並擔任H.265/HEVC SCC和H.266/VVC標準的編輯。她還積極參與多媒體技術的產品化，對幾個百萬用戶的產品做出了重要貢獻。劉博士擁有超過200項授權專利，並發表了超過100篇技術論文。她於2018年被亞太信號與信息處理協會評選為「APSIPA工業傑出領導者」，並於2020年被《福布斯中國》評選為「50位科技女性」。她是《IEEE Transactions on Circuits and Systems for Video Technology》的編輯委員會成員（2018年至今），並於2019年和2020年分別獲得最佳AE獎。她的研究興趣包括視聽、體積、沉浸式及新興媒體的壓縮、智能、傳輸和系統。
張敏於2017年獲得南京科技大學科學學院的工程學士學位，並於2019年獲得南加州大學維特比工程學院的碩士學位。她於2018年夏季加入媒體通信實驗室（MCL），目前是南加州大學的博士生，指導教授為C.-C. Jay Kuo。她的研究興趣包括與點雲處理和分析相關的問題，即點雲分類、註冊、分割和檢測，涉及3D計算機視覺、機器學習和感知領域。
Pranav Kadam於2020年獲得南加州大學的電機工程碩士學位，並於2018年獲得印度薩維特里拜·普赫勒大學的電子與電信工程學士學位。他目前正在南加州大學攻讀電機工程博士學位，並積極參與點雲分析和處理方法的研究與開發。他的研究興趣包括3D計算機視覺、機器學習和感知。
C.-C. Jay Kuo於1987年在麻省理工學院獲得電機工程博士學位。他目前擔任南加州大學電機與計算機工程及計算機科學的威廉·M·霍格教授、傑出教授，以及南加州大學多媒體通信實驗室（MCL）的主任。Kuo博士是美國科學促進會（AAAS）、電氣與電子工程師學會（IEEE）、全國發明家學院（NAI）及國際光學工程師學會（SPIE）的會士。他因其研究貢獻獲得多項獎項，包括2010年電子影像年度科學家獎、2010-11年富布萊特-諾基亞信息與通信技術傑出講座、2011年潘文淵傑出研究獎、2019年IEEE計算機學會Edward J. McCluskey技術成就獎、2019年IEEE信號處理學會Claude Shannon-Harry Nyquist技術成就獎、2020年IEEE TCMC影響獎、第72屆技術與工程艾美獎（2020年）及2021年IEEE電路與系統學會Charles A. Desoer技術成就獎。
