商品描述
How can machines truly "see" and understand the three-dimensional world around them? This book takes readers to the frontier of 3D data analysis, offering a compelling exploration of how deep learning transforms raw point clouds into structured, actionable insights across robotics, autonomous driving, architecture, and beyond. Rather than providing surface-level explanations, this book presents the technical and conceptual foundations of point cloud understanding, from 3D registration and segmentation to object detection and motion tracking. It illuminates how recent advances in neural architectures, feature extraction, and spatial modeling are enabling machines to process unstructured 3D data with increasing precision and efficiency. Readers will discover how these capabilities are reshaping core technologies in navigation, mapping, and intelligent sensing. Written for researchers, engineers, and graduate students with a background in computer vision, AI, or robotics, the book offers both a rigorous introduction and a deep dive into state-of-the-art solutions. Alongside key methodologies, it addresses open challenges such as noise robustness, cross-domain generalization, and scalability--inviting readers to engage with the pressing questions driving this fast-evolving field. Whether for academic inquiry or real-world deployment, Point Cloud Intelligence equips professionals with the frameworks and tools needed to lead innovation in intelligent 3D perception.
商品描述(中文翻譯)
機器如何真正「看見」並理解周圍的三維世界？本書帶領讀者探索3D數據分析的前沿，深入探討深度學習如何將原始點雲轉化為結構化、可行的見解，應用於機器人技術、自動駕駛、建築等領域。
本書不僅提供表面層次的解釋，而是呈現點雲理解的技術和概念基礎，涵蓋從3D配準和分割到物體檢測和運動追蹤的各個方面。它闡明了近期在神經架構、特徵提取和空間建模方面的進展，如何使機器能夠以日益精確和高效的方式處理非結構化的3D數據。讀者將發現這些能力如何重塑導航、地圖製作和智能感知等核心技術。
本書針對具有計算機視覺、人工智慧或機器人技術背景的研究人員、工程師和研究生，提供了嚴謹的介紹以及對最先進解決方案的深入探討。除了關鍵方法論外，還討論了如噪聲穩健性、跨領域泛化和可擴展性等開放挑戰，邀請讀者參與這個快速發展領域中的重要問題。無論是學術研究還是實際應用，《點雲智能》都為專業人士提供了引領智能3D感知創新的框架和工具。
作者簡介
Yulan Guo is a full Professor with Sun Yat-sen University. He has authored over 200 articles at highly referred journals and conferences, receiving over 20,000 citations in Google Scholar. His research interests lie in spatial intelligence, 3D vision, and robotics. He served as a Senior Area Editor for IEEE Transactions on Image Processing, and an Associate Editor for the Visual Computer, and Computers & Graphics. He also served as an area chair for CVPR 2025/2023/2021, ICCV 2025/2021, ECCV 2024, NeurIPS 2025/2024, and ACM Multimedia 2021. He organized over 10 workshops, challenges, and tutorials in prestigious conferences such as CVPR, ICCV, ECCV, and 3DV. He is a Senior Member of IEEE and ACM. Sheng Ao is currently an Assistant Professor with the School of Informatics, Xiamen University, Xiamen, China. He earned his Ph.D. in the School of Electronics and Communication Engineering from the Sun Yat-Sen University (SYSU) in 2024. His research focuses on 3D computer vision, specifically on localization of large-scale 3D point clouds, mapping, and registration. He has contributed to numerous publications in leading journals and conferences such as IEEE TPAMI, IJCV, CVPR, and NeurIPS. Zhiheng Fu currently is a postdoctoral researcher in the Department of Aeronautical and Aviation Engineering at The HongKong Polytechnic University. He earned his Ph.D. in Computer Science and Software Engineering from the University of Western Australia. He holds a Bachelor of Engineering degree in Electrical Engineering from Northeastern University (NEU) and a Master of Engineering degree in Information and Communication Engineering from the National University of Defense Technology (NUDT). Dr. Fu has published numerous publications in prestigious journals and conferences, including IEEE TIP, PR, ICCV, ECCV, and IJCAI. His current research interests lie in 3D Reconstruction and Generation. Hao Liu is currently serving as a Young Principal Investigator (Zijiang Young Scholar) at the School of Geospatial Artificial Intelligence, East China Normal University (ECNU), China. Prior to this, he was a Research Fellow at the School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore. He obtained his B.E. degree from the University of Electronic Science and Technology of China (UESTC) in 2016, followed by an M.E. degree from National University of Defense Technology (NUDT) in 2018. Subsequently, he earned his Ph.D. degree fromSun Yat-Sen University (SYSU) in 2023. His research focuses on 3D deep learning and NeRF, with specific interests in 3D object detection and multi-object tracking.
作者簡介(中文翻譯)
郭玉蘭教授是中山大學的全職教授。他在高引用的期刊和會議上發表了超過200篇文章，並在Google Scholar上獲得了超過20,000次引用。他的研究興趣包括空間智能、3D視覺和機器人技術。他曾擔任《IEEE影像處理學報》的高級區域編輯，以及《Visual Computer》和《Computers & Graphics》的副編輯。他還擔任了CVPR 2025/2023/2021、ICCV 2025/2021、ECCV 2024、NeurIPS 2025/2024和ACM Multimedia 2021的區域主席。他在CVPR、ICCV、ECCV和3DV等知名會議上組織了超過10場研討會、挑戰賽和教程。他是IEEE和ACM的高級會員。
奧勝目前是中國廈門大學資訊學院的助理教授。他於2024年在中山大學電子與通信工程學院獲得博士學位。他的研究專注於3D計算機視覺，特別是大規模3D點雲的定位、映射和配準。他在IEEE TPAMI、IJCV、CVPR和NeurIPS等領先期刊和會議上發表了多篇論文。
傅志恆目前是香港理工大學航空航天工程系的博士後研究員。他在西澳大利亞大學獲得計算機科學與軟體工程的博士學位。他擁有東北大學的電氣工程學士學位，以及國防科技大學的信息與通信工程碩士學位。傅博士在IEEE TIP、PR、ICCV、ECCV和IJCAI等知名期刊和會議上發表了多篇論文。他目前的研究興趣集中在3D重建和生成。
劉浩目前擔任中國華東師範大學地理空間人工智慧學院的青年首席研究員（自江青年學者）。在此之前，他是新加坡南洋理工大學計算機科學與工程學院的研究員。他於2016年在中國電子科技大學獲得工程學士學位，並於2018年在國防科技大學獲得工程碩士學位。隨後，他於2023年在中山大學獲得博士學位。他的研究專注於3D深度學習和NeRF，特別關注3D物體檢測和多物體追蹤。
