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

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

Author: Florent Poux
Publisher: O’Reilly Media
Year: 2025

3D Data Science with Python introduces a cutting-edge approach to processing, analyzing, and modeling real-world environments using Python-powered 3D point-cloud workflows. Written by industry expert Florent Poux, this book guides readers through the complete pipeline of acquiring, cleaning, structuring, and interpreting 3D spatial data at scale.

You will learn how to transform raw sensor data—such as LiDAR scans, photogrammetry captures, and depth imagery—into high-accuracy digital twins, geometric reconstructions, and data-driven 3D analytics used in modern engineering, robotics, surveying, and simulation systems.

Key Topics Covered

• Fundamentals of 3D data science and spatial computing
• Point cloud acquisition: LiDAR, photogrammetry, SLAM, RGB-D sensors
• Python tools for 3D workflows: Open3D, PCL bindings, NumPy, SciPy, PyTorch3D
• Techniques for denoising, filtering, segmentation, and registration
• Feature extraction and machine learning on 3D data
• Surface reconstruction and meshing
• Building digital twins and digital replicas
• Automation workflows for large-scale 3D datasets
• Real-world applications in robotics, architecture, geospatial science, and industrial inspection

Who This Book Is For

• Data scientists expanding into 3D spatial analytics
• Robotics and autonomous systems engineers
• Geospatial and surveying professionals
• AI/ML engineers working with 3D perception
• Researchers and developers building simulation or digital twin systems

This 2025 edition blends theory, hands-on workflows, and Python-based toolchains to help you confidently build accurate, scalable, and production-ready 3D environments.