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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

Exploring Neural Radiance Fields (NeRF)

In the previous chapter, you learned about Differentiable Volume Rendering where you reconstructed the 3D volume from several multi-view images. With this technique, you modeled a volume consisting of N x N x N voxels. The space requirement for storing this volume scale would therefore be O(N3). This is undesirable, especially if we want to transmit this information over the network. Other methods can overcome such large disk space requirements, but they are prone to smoothing geometry and texture. Therefore, we cannot use them to model very complex or textured scenes reliably.

In this chapter, we are going to discuss a breakthrough new approach to representing 3D scenes, called Neural Radiance Fields (NeRF). This is one of the first techniques to model a 3D scene that requires less constant disk space and at the same time, captures the fine geometry and texture of complex scenes.

In this chapter, you will learn about the following topics...

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