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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
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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

The object pose estimation problem

In this section, we are going to show a concrete example of using differentiable rendering for 3D computer vision problems. The problem is object pose estimation from one single observed image. In addition, we assume that we have the 3D mesh model of the object.

For example, we assume we have the 3D mesh model for a toy cow and teapot, as shown in Figure 4.5 and Figure 4.7 respectively. Now, suppose we have taken one image of the toy cow and teapot. Thus, we have one RGB image of the toy cow, as shown in Figure 4.6, and one silhouette image of the teapot, as shown in Figure 4.8. The problem is then to estimate the orientation and location of the toy cow and teapot at the moments when these images are taken.

Because it is cumbersome to rotate and move the meshes, we choose instead to fix the orientations and locations of the meshes and optimize the orientations and locations of the cameras. By assuming that the camera orientations are always...

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