3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods

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.

本書介紹了點雲（point cloud）的應用於工業領域的應用以及最常用的數據集。主要聚焦於三個計算機視覺任務：點雲分類、分割和配准，這些任務對於任何基於點雲的系統都是基礎性的。傳統點雲處理方法的概述有助於讀者快速建立背景知識，而基於點雲的深度學習方法則包括對過去幾年突破的全面分析。接著，全新的可解釋機器學習方法被詳細介紹，這些方法輕量且易於訓練。提供了定量和定性性能評估，並進行了三種方法之間的比較和分析，以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視覺研究人員自然傾向於開發點雲處理的深度學習方法。自2017年以來，基於點雲的深度學習已經越來越受歡迎，這個領域的會議論文數量也在不斷增加。與2D圖像不同，點雲沒有特定的順序，這使得基於點雲的深度學習處理變得非常具有挑戰性。此外，由於點雲的幾何特性，傳統方法在工業領域仍被廣泛使用。因此，本書旨在通過提供傳統方法和最新深度學習方法的全面概述，使讀者熟悉這個領域。

本書的主要部分著重於可解釋機器學習作為一種不同於深度學習的方法。可解釋機器學習方法在傳統方法和深度學習方法之上提供了一系列優勢。這是本書的主要亮點和創新之處。通過使用我們的方法解決三個研究任務（3D物體識別、分割和配准），讀者將對如何以不同的方式解決問題有所了解，並可以將這些框架應用於其他3D計算機視覺任務，從而為他們自己的未來研究提供靈感。

本書提供了大量關於三個3D計算機視覺任務（物體識別、分割、檢測和配准）的實驗、分析和比較，讓讀者學習如何解決困難的計算機視覺問題。

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.

Shan Liu在清華大學獲得電子工程學士學位，並分別在南加州大學獲得電氣工程碩士和博士學位。她目前是騰訊的杰出科學家和騰訊媒體實驗室的總經理。她曾任職於美光美國的媒體技術部門主管，並曾在MERL和Sony等公司工作。Liu博士在國際標準方面有超過十年的積極貢獻。她的許多技術提案被採納為各種標準，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，對幾個百萬用戶產品做出了重要貢獻。Liu博士擁有200多項授予的專利和100多篇技術論文。她於2018年被亞太信號與信息處理協會授予“APSIPA工業杰出領袖”稱號，並於2020年被福布斯中國評為“50位科技女性”。她是IEEE視頻技術電路與系統交易的編輯委員會成員（2018至今），並分別於2019年和2020年獲得最佳AE獎。她的研究興趣包括音視頻、體積、沉浸和新興媒體壓縮、智能、傳輸和系統。

Min Zhang在南京理工大學科學學院獲得學士學位，並在南加州大學Viterbi工程學院分別於2017年和2019年獲得碩士學位。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，由C.-C. Jay Kuo教授指導。她的研究興趣包括點雲處理和分析相關問題，例如點雲分類、配准、分割和檢測，在3D計算機視覺、機器學習和感知領域。

Pranav Kadam在南加州大學獲得電氣工程碩士學位，並在印度普內大學獲得電子和通信工程學士學位。他目前正在南加州大學攻讀電氣工程博士學位。他積極參與點雲分析和處理方法的研究和開發。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在1987年從麻省理工學院獲得電氣工程博士學位。他目前是南加州大學的William M. Hogue教授、電機與計算機工程和計算機科學的傑出教授，並擔任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協會（AAAS）、電氣和電子工程師學會（IEEE）、國家發明家學會（NAI）和國際光學工程師學會（SPIE）的會士。他因其研究貢獻獲得了多個獎項，包括2010年電子成像科學家年度獎、2010-11年富布萊特-諾基亞杰出信息和通信技術主席、2011年潘文元傑出研究獎、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屆年度技術和工程艾美獎（2020年）和2021年IEEE電路和系統學會Charles A. Desoer技術成就獎。