Description

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.