3D DEEP LEARNING WITH PYTHON design and develop your computer vision model with 3D data using PyTorch3D and more /

Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with ease Key Features Understand 3D data processing with rendering, PyTorch optimization, and heterogeneous batching Implement differentiable rendering conc...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ma, Xudong
Otros Autores: Hegde, Vishakh, Yolyan, Lilit
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [S.l.] : PACKT PUBLISHING LIMITED, 2022.
Edición:1st edition.
Temas:
Three-dimensional imaging.
Computer vision.
Deep learning (Machine learning)
Python (Computer program language)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Contributors
  • Table of Contents
  • Preface
  • PART 1: 3D Data Processing Basics
  • Chapter 1: Introducing 3D Data Processing
  • Technical requirements
  • Setting up a development environment
  • 3D data representation
  • Understanding point cloud representation
  • Understanding mesh representation
  • Understanding voxel representation
  • 3D data file format
  • Ply files
  • 3D data file format
  • OBJ files
  • Understanding 3D coordination systems
  • Understanding camera models
  • Coding for camera models and coordination systems
  • Summary
  • Chapter 2: Introducing 3D Computer Vision and Geometry
  • Technical requirements
  • Exploring the basic concepts of rendering, rasterization, and shading
  • Understanding barycentric coordinates
  • Light source models
  • Understanding the Lambertian shading model
  • Understanding the Phong lighting model
  • Coding exercises for 3D rendering
  • Using PyTorch3D heterogeneous batches and PyTorch optimizers
  • A coding exercise for a heterogeneous mini-batch
  • Understanding transformations and rotations
  • A coding exercise for transformation and rotation
  • Summary
  • PART 2: 3D Deep Learning Using PyTorch3D
  • Chapter 3: Fitting Deformable Mesh Models to Raw Point Clouds
  • Technical requirements
  • Fitting meshes to point clouds
  • the problem
  • Formulating a deformable mesh fitting problem into an optimization problem
  • Loss functions for regularization
  • Mesh Laplacian smoothing loss
  • Mesh normal consistency loss
  • Mesh edge loss
  • Implementing the mesh fitting with PyTorch3D
  • The experiment of not using any regularization loss functions
  • The experiment of using only the mesh edge loss
  • Summary
  • Chapter 4: Learning Object Pose Detection and Tracking by Differentiable Rendering
  • Technical requirements
  • Why we want to have differentiable rendering
  • How to make rendering differentiable
  • What problems can be solved by using differentiable rendering
  • The object pose estimation problem
  • How it is coded
  • An example of object pose estimation for both silhouette fitting and texture fitting
  • Summary
  • Chapter 5: Understanding Differentiable Volumetric Rendering
  • Technical requirements
  • Overview of volumetric rendering
  • Understanding ray sampling
  • Using volume sampling
  • Exploring the ray marcher
  • Differentiable volumetric rendering
  • Reconstructing 3D models from multi-view images
  • Summary
  • Chapter 6: Exploring Neural Radiance Fields (NeRF)
  • Technical requirements
  • Understanding NeRF
  • What is a radiance field?
  • Representing radiance fields with neural networks
  • Training a NeRF model
  • Understanding the NeRF model architecture
  • Understanding volume rendering with radiance fields
  • Projecting rays into the scene
  • Accumulating the color of a ray
  • Summary
  • PART 3: State-of-the-art 3D Deep Learning Using PyTorch3D

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