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3D Deep Learning with Python

You're reading from  3D Deep Learning with Python

Product type Book
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247823
Pages 236 pages
Edition 1st Edition
Languages
Authors (3):
Xudong Ma Xudong Ma
Profile icon Xudong Ma
Vishakh Hegde Vishakh Hegde
Profile icon Vishakh Hegde
Lilit Yolyan Lilit Yolyan
Profile icon Lilit Yolyan
View More author details

Table of Contents (16) Chapters

Preface 1. PART 1: 3D Data Processing Basics
2. Chapter 1: Introducing 3D Data Processing 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

Performing End-to-End View Synthesis with SynSin

This chapter is dedicated to the latest state-of-the-art view synthesis model called SynSin. View synthesis is one of the main directions in 3D deep learning, which can be used in multiple different domains such as AR, VR, gaming, and more. The goal is to create a model for the given image as an input to reconstruct a new image from another view.

In this chapter, first, we will explore view synthesis and the existing approaches to solving this problem. We will discuss all advantages and disadvantages of these techniques.

Second, we are going to dive deeper into the architecture of the SynSin model. This is an end-to-end model that consists of three main modules. We will discuss each of them and understand how these modules help to solve view synthesis without any 3D data.

After understanding the whole structure of the model, we will move on to hands-on practice, where we will set up and work with the model to better understand...

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