Florent Poux. 3D Data Science with Python: Building Accurate Digital Environments with 3D Point Cloud Workflows

O’Reilly Media, Inc., 2025. — 687 p. — ISBN: 978-1-098-16133-0.Our physical world is grounded in three dimensions. To create technology that can reason about and interact with it, our data must be 3D too. This practical guide offers data scientists, engineers, and researchers a hands-on approach to working with 3D data using Python. From 3D reconstruction to 3D deep learning techniques, you'll learn how to extract valuable insights from massive datasets, including point clouds, voxels, 3D CAD models, meshes, images, and more.Dr. Florent Poux helps you leverage the potential of cutting-edge algorithms and spatial AI models to develop production-ready systems with a focus on automation.You'll get the 3D data science knowledge and code to:
Understand core concepts and representations of 3D data.
Load, manipulate, analyze, and visualize 3D data using powerful Python libraries.
Apply advanced AI algorithms for 3D pattern recognition (supervised and unsupervised).
Use 3D reconstruction techniques to generate 3D datasets.
Implement automated 3D modeling and generative AI workflows.
Explore practical applications in areas like computer vision/graphics, geospatial intelligence, scientific computing, robotics, and autonomous driving.
Build accurate digital environments that spatial AI solutions can leverage.Who Should Read This Book?This book is a practical reference for data scientists, engineers, and anyone curious about working with 3D data. It assumes very little, and you’ll find value even without any understanding of Python programming and little familiarity with fundamental data science concepts. Moreover, no prior experience with 3D data processing is necessary. I will guide you through the essential libraries and techniques step-by-step, ensuring that you can apply the knowledge to real-world scenarios in a 0-to-1 fashion.