3D Data Science with Python: Building Accurate Digital Environments with 3D Point Cloud Workflows (1)

By: Florent Poux

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Synopsis: 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 dataLoad, manipulate, analyze, and visualize 3D data using powerful Python librariesApply advanced AI algorithms for 3D pattern recognition (supervised and unsupervised)Use 3D reconstruction techniques to generate 3D datasetsImplement automated 3D modeling and generative AI workflowsExplore practical applications in areas like computer vision/graphics, geospatial intelligence, scientific computing, robotics, and autonomous drivingBuild accurate digital environments that spatial AI solutions can leverageFlorent Poux is an esteemed authority in the field of 3D data science who teaches and conducts research for top European universities. He's also head professor at the 3D Geodata Academy and innovation director for French Tech 120 companies.