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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

Book Description

With this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time.

Complete with step-by-step explanations of essential concepts and practical examples, this book lets you explore and gain a thorough understanding of state-of-the-art 3D deep learning. You'll see how to use PyTorch3D for basic 3D mesh and point cloud data processing, including loading and saving ply and obj files, projecting 3D points into camera coordination using perspective camera models or orthographic camera models, rendering point clouds and meshes to images, and much more. As you implement some of the latest 3D deep learning algorithms, such as differential rendering, Nerf, synsin, and mesh RCNN, you'll realize how coding for these deep learning models becomes easier using the PyTorch3D library.

By the end of this deep learning book, you'll be ready to implement your own 3D deep learning models confidently.

What you will learn

Who this book is for

This book is for beginner to intermediate-level machine learning practitioners, data scientists, ML engineers, and DL engineers who are looking to become well-versed with computer vision techniques using 3D data.

3D Deep Learning with Python

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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 easeKey FeaturesUnderstand 3D data processing with rendering, PyTorch optimization, and heterogeneous batchingImplement differentiable rendering concept

See all description...

Author(s): Ma, Xudong • Hegde, Vishakh • Yolyan, Lilit

Publisher: Packt Publishing

Pub. Date: 2022

pages: 236

Language: lang_en

ISBN: 978-1-80324-782-3

eISBN: 978-1-80323-368-0

Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with easeKey FeaturesUnderstand 3D data processing with rendering, PyTorch optimization, and heterogeneous batchingImplement differentiable rendering concept

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 concepts with practical examples
  • Discover how you can ease your work with the latest 3D deep learning techniques using PyTorch3D

Book Description

With this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time.

Complete with step-by-step explanations of essential concepts and practical examples, this book lets you explore and gain a thorough understanding of state-of-the-art 3D deep learning. You'll see how to use PyTorch3D for basic 3D mesh and point cloud data processing, including loading and saving ply and obj files, projecting 3D points into camera coordination using perspective camera models or orthographic camera models, rendering point clouds and meshes to images, and much more. As you implement some of the latest 3D deep learning algorithms, such as differential rendering, Nerf, synsin, and mesh RCNN, you'll realize how coding for these deep learning models becomes easier using the PyTorch3D library.

By the end of this deep learning book, you'll be ready to implement your own 3D deep learning models confidently.

What you will learn

  • Develop 3D computer vision models for interacting with the environment
  • Get to grips with 3D data handling with point clouds, meshes, ply, and obj file format
  • Work with 3D geometry, camera models, and coordination and convert between them
  • Understand concepts of rendering, shading, and more with ease
  • Implement differential rendering for many 3D deep learning models
  • Advanced state-of-the-art 3D deep learning models like Nerf, synsin, mesh RCNN

Who this book is for

This book is for beginner to intermediate-level machine learning practitioners, data scientists, ML engineers, and DL engineers who are looking to become well-versed with computer vision techniques using 3D data.

See all description...