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3D Deep Learning with Python
3D Deep Learning with Python

3D Deep Learning with Python: Design and develop your computer vision model with 3D data using PyTorch3D and more

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Profile Icon Xudong Ma Profile Icon Farrugia Profile Icon Vishakh Hegde Profile Icon Lilit Yolyan
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3D Deep Learning with Python

Introducing 3D Data Processing

In this chapter, we are going to discuss some basic concepts that are very fundamental to 3D deep learning and that will be used frequently in later chapters. We will begin by learning about the most frequently used 3D data formats, as well as the many ways that we are going to manipulate them and convert them to different formats. We will start by setting up our development environment and installing all the necessary software packages, including Anaconda, Python, PyTorch, and PyTorch3D. We will then talk about the most frequently used ways to represent 3D data – for example, point clouds, meshes, and voxels. We will then move on to the 3D data file formats, such as PLY and OBJ files. We will then discuss 3D coordination systems. Finally, we will discuss camera models, which are mostly related to how 3D data is mapped to 2D images.

After reading this chapter, you will be able to debug 3D deep learning algorithms easily by inspecting output data files. With a solid understanding of coordination systems and camera models, you will be ready to build on that knowledge and learn about more advanced 3D deep learning topics.

In this chapter, we’re going to cover the following main topics:

  • Setting up a development environment and installing Anaconda, PyTorch, and PyTorch3D
  • 3D data representation
  • 3D data formats – PLY and OBJ files
  • 3D coordination systems and conversion between them
  • Camera models – perspective and orthographic cameras

Technical requirements

In order to run the example code snippets in this book, you will need to have a computer ideally with a GPU. However, running the code snippets with only CPUs is possible.

The recommended computer configuration includes the following:

  • A GPU such as the GTX series or RTX series with at least 8 GB of memory
  • Python 3
  • The PyTorch library and PyTorch3D libraries

The code snippets for this chapter can be found at https://github.com/PacktPublishing/3D-Deep-Learning-with-Python.

Setting up a development environment

Let us first set up a development environment for all the coding exercises in this book. We recommend using a Linux machine for all the Python code examples in this book:

  1. We will first set up Anaconda. Anaconda is a widely used Python distribution that bundles with the powerful CPython implementation. One advantage of using Anaconda is its package management system, enabling users to create virtual environments easily. The individual edition of Anaconda is free for solo practitioners, students, and researchers. To install Anaconda, we recommend visiting the website, anaconda.com, for detailed instructions. The easiest way to install Anaconda is usually by running a script downloaded from their website. After setting up Anaconda, run the following command to create a virtual environment of Python 3.7:
    $ conda create -n python3d python=3.7

This command will create a virtual environment with Python version 3.7. In order to use this virtual environment, we need to activate it first by running the command:

  1. Activate the newly created virtual environments with the following command:
    $ source activate python3d
  2. Install PyTorch. Detailed instructions on installing PyTorch can be found on its web page at www.pytorch.org/get-started/locally/. For example, I will install PyTorch 1.9.1 on my Ubuntu desktop with CUDA 11.1, as follows:
    $ conda install pytorch torchvision torchaudio cudatoolkit-11.1 -c pytorch -c nvidia
  3. Install PyTorch3D. PyTorch3D is an open source Python library for 3D computer vision recently released by Facebook AI Research. PyTorch3D provides many utility functions to easily manipulate 3D data. Designed with deep learning in mind, almost all 3D data can be handled by mini-batches, such as cameras, point clouds, and meshes. Another key feature of PyTorch3D is the implementation of a very important 3D deep learning technique, called differentiable rendering. However, the biggest advantage of PyTorch3D as a 3D deep learning library is its close ties to PyTorch.

PyTorch3D may need some dependencies, and detailed instructions on how to install these dependencies can be found on the PyTorch3D GitHub home page at github.com/facebookresearch/pytorch3d. After all the dependencies have been installed by following the instructions from the website, installing PyTorch3D can be easily done by running the following command:

$ conda install pytorch3d -c pytorch3d

Now that we have set up the development environment, let’s go ahead and start learning data representation.

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Key benefits

  • 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

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.

Who is this book 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.

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
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Table of Contents

15 Chapters
PART 1: 3D Data Processing Basics Chevron down icon Chevron up icon
Chapter 1: Introducing 3D Data Processing Chevron down icon Chevron up icon
Chapter 2: Introducing 3D Computer Vision and Geometry Chevron down icon Chevron up icon
PART 2: 3D Deep Learning Using PyTorch3D Chevron down icon Chevron up icon
Chapter 3: Fitting Deformable Mesh Models to Raw Point Clouds Chevron down icon Chevron up icon
Chapter 4: Learning Object Pose Detection and Tracking by Differentiable Rendering Chevron down icon Chevron up icon
Chapter 5: Understanding Differentiable Volumetric Rendering Chevron down icon Chevron up icon
Chapter 6: Exploring Neural Radiance Fields (NeRF) Chevron down icon Chevron up icon
PART 3: State-of-the-art 3D Deep Learning Using PyTorch3D Chevron down icon Chevron up icon
Chapter 7: Exploring Controllable Neural Feature Fields Chevron down icon Chevron up icon
Chapter 8: Modeling the Human Body in 3D Chevron down icon Chevron up icon
Chapter 9: Performing End-to-End View Synthesis with SynSin Chevron down icon Chevron up icon
Chapter 10: Mesh R-CNN Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(5 Ratings)
5 star 60%
4 star 20%
3 star 20%
2 star 0%
1 star 0%
Steven Fernandes Dec 12, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is divided into three parts. The first part introduces 3D data processing basics. The authors introduce Ply, OBJ 3D data file formats and coding exercises for 3D rendering using PyTorch3D. The second part introduces 3D deep learning. This includes object pose detection by differentiable rendering, differentiable volumetric rendering and neural radiance fields. The final part introduces current deep learning using PyTorch 3D. This includes training GIRAFFEE model, understanding linear blend Skinning technique and SMPL model. The final 2 chapters includes end-t-end view synthesis with SynSin and Mesh R-CNN.
Amazon Verified review Amazon
Daniel Armstrong May 21, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Overall it was a good book. It covered everything you would expect in a 3D deep learning book (point clouds, NeRFs, GANs, Mesh R-CNN, and that weird puffy cow) I haven't had any experience doing 3d deep learning, so this book was not an easy read for me. Given the complexity of the subject, its not that surprising. My advise to anyone reading it is to read it a few times, don't get wrapped up in concepts that you might not understand, they might become clearer when you come back to the subject. Personally I feel the subject of 3D deep learning is not quite ready for prime time, but with recent advancements with models like stable diffusion I think we are are going to see some amazing tools come out of this field.
Amazon Verified review Amazon
Dror Nov 23, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Deep learning has taken the world by storm and revolutionized entire fields such as computer vision and natural language processing. While there are already many good books on various aspects of deep learning, this book is a wonderful and rather unique resource for learning 3D deep learning.The book begins with covering basic concepts in 3D data processing, and progressively moves to cover essential tools and modern techniques for 3D computer vision and geometry. It provides a great introduction to PyTorch3D - an open-source library for 3D deep learning - and provides detailed and illuminating descriptions of state-of-the-art methods in 3D computer vision, such as NeRF, SynSin and Mesh R-CNN.While the book is rather short (about 220 pages), it is concise and serves as a great introduction to the fascinating world of 3D data processing with deep learning. I'd recommend it mostly to beginners and intermediate-level machine learning practitioners who are interested in mastering the different aspects of 3D deep learning. Note that it's also best to have access to a modern GPU to get the most out of this book, although this isn't a strict requirement.All in all, highly recommended!
Amazon Verified review Amazon
Om S Feb 10, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book "3D Deep Learning with Python: Design and develop your computer vision model with 3D data using PyTorch3D" provides a comprehensive overview of the various techniques and models used in the field of 3D deep learning. The book covers a range of topics, including 3D data file formats (ply and obj), 3D coordination systems, camera models, and basic rendering concepts.The book also explores the basics of PyTorch optimization, heterogeneous batching, and the use of deformable mesh models for fitting. The differentiable rendering concept and differentiable volume rendering are also explained in detail. The book then goes on to cover advanced topics such as Neural Radiance Fields (NeRF), GIRAFFE, human body 3D fitting using SMPL models, end-to-end view synthesis from a single image (Synsin), and Mesh RCNN.Overall, this book is a comprehensive guide to the field of 3D deep learning and provides readers with a solid foundation in the various concepts, techniques, and models used in this field. The explanations are clear and well-structured, making it an accessible resource for both beginners and more experienced practitioners.
Amazon Verified review Amazon
Simon Jul 01, 2023
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I tried to download the PyTorch3d as usual there is problems for Mac uses trying to learn 3d learning!! Be warne3d
Amazon Verified review Amazon
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