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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
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Vishakh Hegde
Lilit Yolyan Lilit Yolyan
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Lilit Yolyan
David Farrugia David Farrugia
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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

Why we want to have differentiable rendering

The physical process of image formation is a mapping from 3D models to 2D images. As shown in the example in Figure 4.1, depending on the positions of the red and blue spheres in 3D (two possible configurations are shown on the left-hand side), we may get different 2D images (the images corresponding to the two configurations are shown on the right-hand side).

Figure 4.1: The image formation process is a mapping from the 3D models to 2D images

Many 3D computer vision problems are a reversal of image formation. In these problems, we are usually given 2D images and need to estimate the 3D models from the 2D images. For example, in Figure 4.2, we are given the 2D image shown on the right-hand side and the question is, which 3D model is the one that corresponds to the observed image?

Figure 4.2: Many 3D computer vision problems are based on 2D images given to estimate 3D models

According to some...

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