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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

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247823
Length 236 pages
Edition 1st Edition
Languages
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Authors (4):
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Xudong Ma Xudong Ma
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Xudong Ma
Vishakh Hegde Vishakh Hegde
Author Profile Icon Vishakh Hegde
Vishakh Hegde
Lilit Yolyan Lilit Yolyan
Author Profile Icon Lilit Yolyan
Lilit Yolyan
David Farrugia David Farrugia
Author Profile Icon David Farrugia
David Farrugia
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Toc

Table of Contents (16) Chapters Close

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 15. Other Books You May Enjoy

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

This part of the book will be all about using PyTorch3D to implement state-of-the-art 3D deep learning models and algorithms. 3D computer vision technologies are making rapid progress in recent times and we will learn how to implement and use these state-of-the-art 3D deep learning models in the best way possible.

This part includes the following chapter:

  • 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
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