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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

Understanding Differentiable Volumetric Rendering

In this chapter, we are going to discuss a new way of differentiable rendering. We are going to use a voxel 3D data representation, unlike the mesh 3D data representation we used in the last chapter. Voxel 3D data representation has certain advantages compared to mesh models. For example, it is more flexible and highly structured.

To understand volumetric rendering, we need to understand several important concepts, such as ray sampling, volumes, volume sampling, and ray marching. All these concepts have corresponding PyTorch3D implementations. We will discuss each of these concepts using explanations and coding exercises.

After we understand the preceding basic concepts of volumetric rendering, we can then see easily that all the operations mentioned already are already differentiable. Volumetric rendering is naturally differentiable. Thus, by then, we will be ready to use differentiable volumetric rendering for some real applications...

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