- 3D数据科学与Python(影印版)(英文版)
- - 作者：(美)弗洛朗·普|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576620030
  - 出版日期：2025/07/01
  - 页数：658
- 售价：75.2

内容大纲
    我们的物理世界是建立在三维空间之中的。为了创造能够理解并与之交互的技术，我们的数据也必须是三维的。这本实用指南为数据科学家、工程师、研究人员提供了使用Python处理3D数据的实践方法。从3D重建到3D深度学习技术，你将学习如何从海量数据集中提取有价值的洞察，包括点云、体素、3D CAD模型、网格、图像等。
    Florent Poux博士将帮助你借助前沿算法和空间AI模型的潜力，开发以自动化为核心的生产就绪系统（production-ready system）。通过本书，你将习得3D数据科学的知识与代码，实现以下目标：
    理解3D数据的核心概念和表示方法
    使用强大的Python库加载、操作、分析和可视化3D数据
    应用先进的AI算法进行3D模式识别（包括监督与非监督方法）
    使用3D重建技术生成3D数据集
    实现自动化3D建模与生成式AI工作流
    探索在计算机视觉/图形、地理空间情报、科学计算、机器人技术、自动驾驶等领域的实际应用
    构建服务于空间AI解决方案的精准数字环境
作者介绍
弗洛朗·普是3D数据科学领域的知名专家，常年在欧洲顶尖高校从事教学与研究工作。他还是3D地理数据学院（3D Geodata Academy）的首席教授以及法国Tech 120企业的创新总监。
目录
Table of Contents
Foreword
Preface
1. Introduction to 3D Data Science
  3D Data Science in Brief
    Dimensions and 3D Data Science
    Spatial AI: From Reality to Virtuality
  3D Data: Fundamental Building Blocks
    Geometry, Topology, and Semantics
    Integrating Geometry, Topology, and Semantics
    Introduction to 3D Point Clouds
  The 3D Data Science Modular Workflow
    Data Acquisition
    Preprocessing
    Registration
    3D Data Classification (Semantic Injection)
    Structuration/Modeling
    3D Data Analysis
    3D Data Visualization
    Application (Software) Development
    The Case for Automation
    Workflow Challenges in 3D Data Science
    3D Data Science in the Industry
    Summary
2. Resources and Software Essentials
  Fundamental Resources
    Mathematics
    Computer Science
    3D Data Expertise
      Artificial Intelligence for 3D
      Hardware Recommendations for 3D
        Local 3D Development
        Cloud Computing
    Essential Software and Tools for 3D
      3D Reconstruction Software
      3D Data Processing Software
      3D Visualization Software
    Summary
3. 3D Python and 3D Data Setup
  3D Python Setup and Libraries
    Choice of OS
    Environment Setup
    Base Python Libraries
    3D Python Libraries
    The Python IDE
    Creating a 3D Python Program
    Importing 3D Data in Python
    Extracting Specific Attributes
    Conducting Attribute-based Data Analysis
    3D Data Visualization and Export

  3D Reconstruction Methods
    Real-World 3D Reconstruction (Sensor-Based)
    Creative 3D Reconstruction
  3D Dataset: Curation
    3D Data from Image-based Reconstruction
    Multimodal Web Scraping
    Summary
4. 3D Data Representation and Structuration
  3D Data Representations
    3D Point Clouds
    Image-based Representations
    Volumetric (Voxel) Models
    High-level 3D Data Representation
    3D Surface Models
  3D Data Canonical Link
    Mesh to Point Cloud
    Voxel to Point Cloud
    Raster to Point Cloud
  3D Data Structures: k-d Trees, Octrees, BVH
    k-d Trees
    Octrees
    File Organization
    Summary
5. Developing a Multimodal 3D Viewer with Python
  3D Python and Code Setup
  3D Data Curation
  3D Data Preparation
    Initial Profiling
    3D Data Downsampling
    Data Preprocessing
    3D Data Visualization
  Multimodal 3D Experience
    Point of Interest Query
    Manual Boundary Selection
    Find High and Low Points
    Point Cloud Voxelization
    Built Coverage Extraction
    Summary
6. Point Cloud Data Engineering
  Fundamentals
    Initial Preprocessing
    Feature Extraction Fundamentals
  Strategies for Point Cloud Feature Extraction
    Global Feature Extraction
    Local Feature Extraction
    Principal Component Analysis
    Python and Data Preparation
      Cluster Identification with pandas
      3D Data Normalization
        Extracting the Principal Components

    3D Visualization of PCA
  3D Data Registration: Unifying Perspectives
    3D Data Registration Fundamentals
      Registration Initialization
      Coarse Registration
      Iterative Closest Point
      Fine Registration: ICP
    Summary
7. Building 3D Analytical Apps
  3D Project Environment Preparation
    Gathering Datasets
    Python and Environment Setup
  3D Data Fundamentals with PyVista
  3D Data Structure Creation (KDTree)
  Covariance Matrix, Eigenvalues, and Eigenvectors
  Planarity, Linearity, Omnivariance, Verticality, Normals
  Neighborhood Definition and Selection
  Automation and Scaling
  Interactive Thresholding
  3D Data Results Export
  Summary
8. 3D Data Analysis
  Types of 3D Data Analysis
    3D Descriptive Data Analysis
    3D Exploratory Data Analysis
    3D Predictive Data Analysis
    3D Prescriptive Data Analysis
    Additional Considerations
  3D Data Analytical Tools
    Environment and Data Preparation
    Metadata Analysis and Data Profiling
    Geometry and Shape Analysis
    Statistical Analysis
    Attribute Analysis
  3D Diagnostic Tools
    3D Deviation Analysis: Planar Case
    3D Deviation Analysis: Mesh Case
    Summary
9. 3D Shape Recognition
  RANSAC from Scratch: 3D Planar Shape Recognition
    RANSAC
    Data and Environment Setup
    Geometric Model Selection
    3D Shape Fitting
    Iteration and Function Definition
    Application 1: RANSAC for Segmentation Tasks
    Application 2: RANSAC for Analytical Tasks
    Application 3: RANSAC for Modeling Tasks
  Region Growing for 3D Shape Detection
    Region Growing Principles

    Region Growing: Real-World Setup
    Region Growing: Implementation
    A Hybrid Approach: RANSAC and Region Growing
    Summary
10. 3D Modeling: Advanced Techniques
  High-Fidelity Meshing
    General Overview of High-Fidelity 3D Meshes
    The Mission
    Data Preparation
    Choose a Meshing Strategy
    Other 3D Meshing Strategies
    3D Meshing with Python
      Levels of Detail Creation
      Visualization and Software
  3D Voxels and Voxelization
    Python Environment Initialization
    Loading the Data
    Creating the Voxel Grid
    Generating the Voxel Cubes (3D Meshes)
    Export the Mesh Object (.ply or.obj)
  Parametric Modeling
    CadQuery and Environment Setup
    I/O for Parametric Models: 2D (DXF) and 3D (STL)
    Parametric Modeling Techniques
      The Boolean Operations
      Modeling Various Pieces
    Conclusion
  Monocular Image-based 3D Modeling: Depth Estimation and Reconstruction
    Setting Up the Environment and Installing the Libraries
    Gathering a Dataset
    Image Preprocessing and Model Setup
    Depth Estimation Predictions from the Model
    Point Cloud Generation
    Defining the Camera Intrinsics
  3D Modeling: 3D Point Cloud to Mesh
  Summary
11. 3D Building Reconstruction from LiDAR Data
  Phase 1: 3D Python Setup
    Project Environment Setup
    Project Notebook Setup
  Phase 2: Data Preparation
    Aerial LiDAR Data Curation
    Aerial LiDAR Data Preprocessing
  Phase 3: Experiments
    Unsupervised Point Cloud Segmentation
    3D House Segment Isolation
    2D Building Footprint Extraction
    Semantic and Attribute Extraction
      2D to 3D Vectors
    3D Model Creation: Mesh

  Phase 4: Automation and Scaling
  Summary
12. 3D Machine Learning: Clustering
  Clustering for Unsupervised Segmentation
    Clustering Fundamentals
    Clustering Representativity
    Types of Clustering Algorithms
    k-Means Clustering
      k-Means: Workflow Definition
      3D Python Context Definition
      LiDAR Data Preprocessing
      k-Means Implementation
    DBSCAN for Unsupervised Segmentation
      DBSCAN Principles
      The Strategy
      Experimental Setup
      3D Planar Shape Recognition with RANSAC
      DBSCAN for 3D Point Cloud Segmentation
      The Multi-RANSAC Framework
      Multi-RANSAC Refinement with DBSCAN
      DBSCAN Refinement
    DBSCAN Versus k-Means
    Summary
13. Graphs and Foundation Models for Unsupervised Segmentation
  Connectivity-based Clustering
    The Mission Brief
    Core Principles
    Step 1: Environment Setup
    Step 2: Graph Theory for 3D Clustering
    Step 3: Graph Analytics
    Step 4: Plotting Graphs (Optional)
    Step 5: Connected Components for Point Clouds
    Step 6: Euclidean Clustering for 3D Point Clouds
    Discussion and Perspectives
  The Segment Anything Model
    The Mission
    3D Project Setup
    Segment Anything Model Core Concepts
    3D Point Cloud to Image Projections
    Unsupervised Segmentation with SAM
    Summary
14. Supervised 3D Machine Learning Fundamentals
  From Unsupervised to Supervised Learning
  Supervised Learning Concepts
  Supervised Learning Classification
  3D Semantic Segmentation Example
  3D Point Cloud Semantic Segmentation
    3D Python and Data Setup
    Feature Selection and Preparation
    Metrics and Models

    Inference and Generalization
    Specializing 3D Machine Learning with 3D Deep Learning
    Summary
15. 3D Deep Learning with PyTorch
  3D Deep Learning Backbone
    Network Architecture
    Data Preparation
    AI Model Training
    Serving a Trained Model
    Implementation with PyTorch
      Installing PyTorch (with CUDA)
      Tensors: The Building Blocks
      Neural Network Modules
      Defining a 3D Neural Network
      Hyperparameter Definition
      Optimizer and Loss Functions
      PyTorch DataLoader
      PyTorch Training Loop
      PyTorch Inference
  3D Deep Learning: The Architectures
    3D Convolutional Neural Networks: Voxels
    3D Graph Neural Networks
    Point-based Architectures: PointNet and Point Clouds
    Multiview CNNs
  3D Machine Learning Versus 3D Deep Learning
  Fine-Tuning, Transfer Learning, and 3D Data Augmentation
    Transfer Learning
    Fine-Tuning
    3D Data Augmentation: Expanding the Dataset
    Summary
16. PointNet for 3D Object Classification
  PointNet: A Point-based 3D Deep Learning Architecture
  3D Object Classification
    3D Object Classification Fundamentals
    Environment Setup
    Dataset Curation
    PointNet: Dataset Preparation
    PointNet Architecture Definition
    PointNet Loss Definition
    PointNet Training
    PointNet Metrics and Evaluation
    PointNet Real-World Inference
    Large-Scale Semantic Segmentation Considerations
    Summary
17. The 3D Data Science Workflow
  3D Data Acquisition
  3D Data Preparation and Engineering
    Noise Removal
    Subsampling
    Feature Extraction

  3D Data Modeling
    3D Mesh Reconstruction
    Voxelization of 3D Digital Environments
    k-d Trees
    Octrees
  Semantic Extraction
    Clustering and Unsupervised Segmentation
    Semantic Segmentation
  3D Object Classification
  3D Data Visualization and Analysis
    3D Shape Recognition
    3D Data Analytical Tools
    3D Multimodal Python Viewer
    Summary
18. From 3D Generative AI to Spatial AI
  Advanced 3D Projects
    Generative AI for 3D Reconstruction
    3D Deep Point Cloud Registration
    3D Semantic Modeling
    3D Semantic Extraction with Transformers
    3D Gaussian Splatting for 3D Visualization
  Spatial AI: The Future of 3D Experiences
    3D Scene Understanding with Open Vocabularies
    3D Spatial AI Reasoning
    Conclusion
Index

内容大纲

作者介绍

目录

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