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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
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

Overview of view synthesis

One of the most popular research directions in 3D computer vision is view synthesis. Given the data and the viewpoint, the idea of this research direction is to generate a new image that renders the object from another viewpoint.

View synthesis comes with two challenges. The model should understand the 3D structure and semantic information of the image. By 3D structure, we mean that when changing the viewpoint, we get closer to some objects and far away from others. A good model should handle this by rendering images where some objects are bigger and some are smaller to view - change. By semantic information, we mean that the model should differentiate the objects and should understand what objects are presented in the image. This is important because some objects can be partially included in the image; therefore, during the reconstruction, the model should understand the semantics of the object to know how to reconstruct the continuation of that object...

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