- 三维建模学习算法(英文版)/人工智能与大数据系列
- - 作者：吴素萍//李雷//张博洋|责编:刘志红
  - 出版社：电子工业
  - ISBN：9787121516085
  - 出版日期：2025/11/01
  - 页数：419
- 售价：111.2

内容大纲
基于图像视频的三维建模是3D数字技术的核心内容，可以重建真实3D场景和人物，广泛应用于机器人及自动驾驶等领域，属于跨学科研究领域，具有很高的研究和应用价值。
本书围绕图像视频三维建模的新研究技术和方法展开，重点关注挑战性问题，并进行了系统研究和介绍，包括3D物体建模、3D人面部建模、3D人体姿态建模及通用建模的相关学习算法，是一本系统介绍三维建模先进方法的研究专著。书中描述的所有算法都来自我们的研究成果，与最先进方法进行了比较，验证了有效性和先进性。本书将使人工智能及信息计算机等领域的研究人员、专业人士和研究生受益，对跨学科研究也非常有用。
作者介绍
目录
Chapter 1 Introduction
  1.1 3D Object Modeling
    1.1.1 Single-View 3D Reconstruction
    1.1.2 Multi-View 3D Reconstruction Method
  1.2 3D Face Modeling
    1.2.1 3D Face Keypoint Detection
    1.2.2 3D Face Reconstruction
  1.3 3D Human Body Modeling
    1.3.1 3D Human Pose Estimation
    1.3.2 3D Human Body Reconstruction
  1.4 3D Reconstruction Modeling
  1.5 Outline of the Work
  Bibliography
Chapter 2 3D Object Modeling
  2.1 Single-View 3D Object Modeling
    2.1.1 Multi-Scale Edge-Guided Learning for 3D Reconstruction
    2.1.2 Multi-Granularity Relationship Reasoning Network for High-Fidelity 3D Shape Reconstruction
    2.1.3 3D Shape Reconstruction Based on Dynamic Multi-Branch Information Fusion
    2.1.4 Hierarchical Feature Learning Network for 3D Object Reconstruction
  2.2 Multi-View 3D Object Modeling
    2.2.1 High-Resolution Multi-View Stereo with Dynamic Depth Edge Flow
    2.2.2 Global Contextual Complementary Network for Multi-View Stereo
    2.2.3 Attention-Guided Multi-View Stereo Network for Depth Estimation
    2.2.4 Self-Supervised Edge Structure Learning for Multi-View Stereo and Parallel Optimization
    2.2.5 Layered Decoupled Complementary Networks for Multi-View Stereo
    2.2.6 Global Balanced Networks for Multi-View Stereo
  Bibliography
Chapter 3 3D Face Keypoint Detection
  3.1 Learning Relation-Sensitive Structured Network for Robust Face Alignment
    3.1.1 Introduction
    3.1.2 Proposed Method
    3.1.3 Experiments
    3.1.4 Conclusion
  3.2 Multi-Agent Deep Collaboration Learning for Face Alignment under Different Perspectives
    3.2.1 Introduction
    3.2.2 Proposed Method
    3.2.3 Experiments
    3.2.4 Conclusion
  3.3 Towards Accurate 3D Face Alignment under Extreme Scenarios via Multi-Granularity Perturbation Relearning
    3.3.1 Introduction
    3.3.2 Proposed Method
    3.3.3 Loss Function
    3.3.4 Experiments
    3.3.5 Conclusion
  Bibliography
Chapter 4 3D Face Reconstruction
  4.1 Towards Rich-Detail 3D Face Reconstruction and Dense Alignment via Multi-Scale Detail Augmentation
    4.1.1 Introduction
    4.1.2 Proposed Method
    4.1.3 Experiments

    4.1.4 Conclusion
  4.2 Multi-Attribute Regression Network for Face Reconstruction
    4.2.1 Introduction
    4.2.2 Proposed Method
    4.2.3 Experiments
    4.2.4 Conclusion
  4.3 Geometry Normal Consistency Loss for 3D Face Reconstruction and Dense Alignment
    4.3.1 Introduction
    4.3.2 Proposed Method
    4.3.3 Experiments
    4.3.4 Conclusion
  4.4 Complementary Learning Network for 3D Face Reconstruction and Alignment
    4.4.1 Introduction
    4.4.2 Proposed Method
    4.4.3 Experiments
    4.4.4 Conclusion
  4.5 Graph Structure Reasoning Network for Face Alignment and Reconstruction
    4.5.1 Introduction
    4.5.2 Proposed Method
    4.5.3 Experiments
    4.5.4 Conclusion
  4.6 Unsupervised Shape Enhancement and Factorization Machine Network for 3D Face Reconstruction
    4.6.1 Introduction
    4.6.2 Proposed Method
    4.6.3 Experiments
    4.6.4 Conclusion
  4.7 A Detail Geometry Learning Network for High-Fidelity Face Reconstruction
    4.7.1 Introduction
    4.7.2 Proposed Method
    4.7.3 Experiments
    4.7.4 Conclusion
  4.8 A Bi-Directional Optimization Network for De-Obscured 3D High-Fidelity Face Reconstruction
    4.8.1 Introduction
    4.8.2 Proposed Method
    4.8.3 Experiments
    4.8.4 Conclusion
  Bibliography
Chapter 5 3D Human Pose Estimation
  5.1 Multi-Hybrid Extractor Network for 3D Human Pose Estimation
    5.1.1 Introduction
    5.1.2 Proposed Method
    5.1.3 Experiments
    5.1.4 Conclusion
  5.2 3D Human Pose Estimation Based on Center of Gravity
    5.2.1 Introduction
    5.2.2 Proposed Method
    5.2.3 Experiments
    5.2.4 Conclusion
  5.3 Edge-Angle Structure Constraint Loss for 3D Human Pose Estimation
    5.3.1 Introduction

    5.3.2 Related Works
    5.3.3 Proposed Method
    5.3.4 Experiments
    5.3.5 Conclusion
  Bibliography
Chapter 6 3D Human Body Reconstruction
  6.1 Two-Stage Co-Segmentation Network Based on Discriminative Representation for Recovering Human Mesh from Videos
    6.1.1 Introduction
    6.1.2 Related Works
    6.1.3 Proposed Method
    6.1.4 Experiments
    6.1.5 Conclusion
  6.2 Frame-Level Feature Tokenization Learning for Human Body Pose and Shape Estimation
    6.2.1 Introduction
    6.2.2 Related Works
    6.2.3 Proposed Method
    6.2.4 Experiments
    6.2.5 Conclusion
  6.3 Time-Frequency Awareness Network for Human Mesh Recovery from Videos
    6.3.1 Introduction and Related Works
    6.3.2 Proposed Method
    6.3.3 Experiments
    6.3.4 Conclusion
  6.4 Spatio-Temporal Tendency Reasoning for Human Body Pose and Shape Estimation from Videos
    6.4.1 Introduction and Related Works
    6.4.2 Proposed Method
    6.4.3 Experiments
    6.4.4 Conclusion
  Bibliography
Chapter 7 3D Reconstruction Modeling
  7.1 Replay Attention and Data Augmentation Network for 3D Face and Object Reconstruction
    7.1.1 Introduction
    7.1.2 Related Works
    7.1.3 Proposed Method
    7.1.4 Experiments
    7.1.5 Conclusion
  7.2 A Lightweight Grouped Low-Rank Tensor Approximation Network for 3D Mesh Reconstruction from Videos
    7.2.1 Introduction
    7.2.2 Proposed Method
    7.2.3 Experiments
    7.2.4 Conclusion
  Bibliography

内容大纲

作者介绍

目录

同类热销排行榜

推荐书目