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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
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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

Formulating a deformable mesh fitting problem into an optimization problem

In this section, we are going to talk about how to formulate the mesh fitting problem into an optimization problem. One key observation here is that object surfaces such as pedestrians can always be continuously deformed into a sphere. Thus, the approach we are going to take will start from the surface of a sphere and deform the surface to minimize a cost function.

The cost function should be chosen such that it is a good measurement of how similar the point cloud is to the mesh. Here, we choose the major cost function to be the Chamfer set distance. The Chamfer distance is defined between two sets of points as follows:

The Chamfer distance is symmetric and is a sum of two terms. In the first term, for each point x in the first point cloud, the closest point y in the other point cloud is found. For each such pair x and y, their distance is obtained and the distances for all the...

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