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3D Deep Learning with Python

You're reading from  3D Deep Learning with Python

Product type Book
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247823
Pages 236 pages
Edition 1st Edition
Languages
Authors (3):
Xudong Ma Xudong Ma
Profile icon Xudong Ma
Vishakh Hegde Vishakh Hegde
Profile icon Vishakh Hegde
Lilit Yolyan Lilit Yolyan
Profile icon Lilit Yolyan
View More author details

Table of Contents (16) Chapters

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

Technical requirements

The computation requirements for the code in this chapter are pretty low. However, running this in a Linux environment is recommended since it has better support for certain libraries. However, it is not impossible to run this in other environments. In the coding sections, we describe in detail how to set up the environment to successfully run the code. We will need the following technical requirements for this chapter:

  • Python 2.7
  • Libraries such as opendr, matplotlib, opencv, and numpy.

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

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