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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
Author Profile Icon Xudong Ma
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

Differentiable volumetric rendering

While standard volumetric rendering is used to render 2D projections of 3D data, differentiable volume rendering is used to do the opposite: construct 3D data from 2D images. This is how it works: we represent the shape and texture of the object as a parametric function. This function can be used to generate 2D projections. But, given 2D projections (this is typically multiple views of the 3D scene), we can optimize the parameters of these implicit shape and texture functions so that its projections are the multi-view 2D images. This optimization is possible since the rendering process is completely differentiable, and the implicit functions used are also differentiable.

Reconstructing 3D models from multi-view images

In this section, we are going to show an example of using differentiable volumetric rendering for reconstructing 3D models from multi-view images. Reconstructing 3D models is a frequently sought problem. Usually, the direct ways...

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