3D Data Science with Python, Poux F., 2025

3D Data Science with Python, Poux F., 2025.
    
   This book is a practical reference for data scientists, engineers, and anyone curious about working with 3D data. It assumes very little: it will work beautifully, 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 and challenges, in a о to 1 fashion.




Introduction to Point Cloud Data.
Entities and objects can be described at varying levels of detail. For instance, a point can represent a city, a person, or a country. However, in the field of 3D Data Science, data points are typically collected using a set of sensors. While single raster images or video streams can be useful in certain cases, they may only sometimes provide the necessary depth of information to emulate our 3D visual perception accurately. Therefore, a more comprehensive and diverse data foundation is often required.

Reality Capture devices allow for the capture of comprehensive 3D spatial information in the form of a point cloud, a spatial ensemble consisting of {X, Y, Z} coordinates, along with attributes that represent the recorded environment based on the strengths and limitations of the sensor.

The landscape of suitable hardware, software, and methodological choice has matured to the extent where it is possible to create digital replicas of the real world, ranging from Micro+ Level Scale to country-scale, as illustrated in Figure 1-13.

Contents.
Chapter 1: Introduction to 3D Data Science (available).
Chapter 2: Essentials of 3D Data Science (available).
Chapter 3: 3D Python and Data Setup (unavailable).
Chapter 4: 3D Data Representation and Structuration (unavailable).
Chapter 5: Multi-Modal 3D Viewer Implementation (unavailable).
Chapter 6: Point Cloud Data Engineering (unavailable).
Chapter 7: End-to-End Feature Extraction (unavailable).
Chapter 8: 3D Data Analysis (unavailable).
Chapter g: 3D Scene Diagnosis for Immersive Simulations (unavailable).
Chapter 10: 3D Modelling Advanced Techniques (unavailable).
Chapter 11: Aerial LiDAR 3D Building Modeling (unavailable).
Chapter 12 : 3D Segmentation and Clustering (unavailable).
Chapter 13: Unsupervised Segmentation with Foundation Models (unavailable).
Chapter 14: Artificial Intelligence for 3D Workflows (unavailable).
Chapter 15: 3D Deep Learning with PyTorch (unavailable).
Chapter 16: PointNet for Object Classification (unavailable).
Chapter 17: 3D GenAI, and Spatial AI Cognition: Summary and Perspectives (unavailable).



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