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3D Deep Learning with Python

You're reading from 3D Deep Learning with Python Design and develop your computer vision model with 3D data using PyTorch3D and more

Product type Paperback

Published in Oct 2022

Publisher Packt

ISBN-13 9781803247823

Length 236 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch3D

Concepts

Computer Vision

Authors (4):

Xudong Ma

Vishakh Hegde

Lilit Yolyan

David Farrugia

View More author details

Table of Contents (16) Chapters

Preface

1. PART 1: 3D Data Processing Basics

2. Chapter 1: Introducing 3D Data Processing FREE CHAPTER

3. Chapter 2: Introducing 3D Computer Vision and Geometry

4. PART 2: 3D Deep Learning Using PyTorch3D

5. Chapter 3: Fitting Deformable Mesh Models to Raw Point Clouds

6. Chapter 4: Learning Object Pose Detection and Tracking by Differentiable Rendering

7. Chapter 5: Understanding Differentiable Volumetric Rendering

8. Chapter 6: Exploring Neural Radiance Fields (NeRF)

9. PART 3: State-of-the-art 3D Deep Learning Using PyTorch3D

10. Chapter 7: Exploring Controllable Neural Feature Fields

11. Chapter 8: Modeling the Human Body in 3D

12. Chapter 9: Performing End-to-End View Synthesis with SynSin

13. Chapter 10: Mesh R-CNN

14. Index

Why subscribe?

15. Other Books You May Enjoy

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.

You have been reading a chapter from

3D Deep Learning with Python

Published in: Oct 2022

Publisher: Packt

ISBN-13: 9781803247823

© 2022 Packt Publishing Limited All Rights Reserved

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Authors (4)

Farrugia

Farrugia

David Farrugia is a seasoned data scientist and a Ph.D. candidate in AI at the University of Malta. David Farrugia has worked in diverse industries, including gaming, manufacturing, customer relationship management, affiliate marketing, and anti-fraud. He has an interest in exploring the intersection of business and academic research. He also believes that the emerging field of neuro-symbolic AI has the potential to revolutionize the way we approach AI and solve some of the most complex problems in the world.

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Xudong Ma

Xudong Ma

Xudong Ma is a Staff Machine Learning engineer with Grabango Inc. at Berkeley California. He was a Senior Machine Learning Engineer at Facebook(Meta) Oculus and worked closely with the 3D PyTorch Team on 3D facial tracking projects. He has many years of experience working on computer vision, machine learning and deep learning. He holds a Ph.D. in Electrical and Computer Engineering.

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Lilit Yolyan

Lilit Yolyan

Lilit Yolyan is a machine learning researcher working on her Ph.D. at YSU. Her research focuses on building computer vision solutions for smart cities using remote sensing data. She has 5 years of experience in the field of computer vision and has worked on a complex driver safety solution to be deployed by many well-known car manufacturing companies.

See other products by Lilit Yolyan

Vishakh Hegde

Vishakh Hegde

Vishakh Hegde is a Machine Learning and Computer Vision researcher. He has over 7 years of experience in this field during which he has authored multiple well cited research papers and published patents. He holds a masters from Stanford University specializing in applied mathematics and machine learning, and a BS and MS in Physics from IIT Madras. He previously worked at Schlumberger and Matroid. He is a Senior Applied Scientist at Ambient.ai, where he helped build their weapon detection system which is deployed at several Global Fortune 500 companies. He is now leveraging his expertise and passion to solve business challenges to build a technology startup in Silicon Valley. You can learn more about him on his personal website.

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