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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 PART 1: 3D Data Processing Basics
Chapter 1: Introducing 3D Data Processing Chapter 2: Introducing 3D Computer Vision and Geometry PART 2: 3D Deep Learning Using PyTorch3D
Chapter 3: Fitting Deformable Mesh Models to Raw Point Clouds Chapter 4: Learning Object Pose Detection and Tracking by Differentiable Rendering Chapter 5: Understanding Differentiable Volumetric Rendering Chapter 6: Exploring Neural Radiance Fields (NeRF) PART 3: State-of-the-art 3D Deep Learning Using PyTorch3D
Chapter 7: Exploring Controllable Neural Feature Fields Chapter 8: Modeling the Human Body in 3D Chapter 9: Performing End-to-End View Synthesis with SynSin Chapter 10: Mesh R-CNN Index Other Books You May Enjoy

Summary

In this chapter, we learned about the basic concepts of rendering, rasterization, and shading, including light source models, the Lambertian shading model, and the Phong lighting model. We learned how to implement rendering, rasterization, and shading using PyTorch3D. We also learned how to change the parameters in the rendering process, such as ambient lighting, shininess, and specular colors, and how these parameters would affect the rendering results.

We then learned how to use the PyTorch optimizer. We went through a coding example, where the PyTorch optimizer was used on a PyTorch3D mini-batch. In the last part of the chapter, we learned how to use the PyTorch3D APIs for converting between the different representations or rotations and transformations.

In the next chapter, we will learn some more advanced techniques for using deformable mesh models for fitting real-world 3D data.

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