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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

Concepts

Computer Vision

Authors (3):

Xudong Ma

Vishakh Hegde

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

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Technical requirements

In order to run the example code snippets in this book, you will need to have a computer ideally with a GPU. However, running the code snippets with only CPUs is possible.

The recommended computer configuration includes the following:

A GPU such as the GTX series or RTX series with at least 8 GB of memory
Python 3
The PyTorch library and PyTorch3D libraries

The code snippets for this chapter can be found at https://github.com/PacktPublishing/3D-Deep-Learning-with-Python.

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3D Deep Learning with Python

Published in: Oct 2022 Publisher: Packt ISBN-13: 9781803247823

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Authors (3)

Xudong Ma

Xudong Ma is a Staff Machine Learning engineer with Grabango Inc. at Berkeley California. He was a Senior Machine Learning Engineer at Facebook(Meta) Oculus and worked closely with the 3D PyTorch Team on 3D facial tracking projects. He has many years of experience working on computer vision, machine learning and deep learning. He holds a Ph.D. in Electrical and Computer Engineering.

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Vishakh Hegde

Vishakh Hegde is a Machine Learning and Computer Vision researcher. He has over 7 years of experience in this field during which he has authored multiple well cited research papers and published patents. He holds a masters from Stanford University specializing in applied mathematics and machine learning, and a BS and MS in Physics from IIT Madras. He previously worked at Schlumberger and Matroid. He is a Senior Applied Scientist at Ambient.ai, where he helped build their weapon detection system which is deployed at several Global Fortune 500 companies. He is now leveraging his expertise and passion to solve business challenges to build a technology startup in Silicon Valley. You can learn more about him on his personal website.

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Lilit Yolyan

Lilit Yolyan is a machine learning researcher working on her Ph.D. at YSU. Her research focuses on building computer vision solutions for smart cities using remote sensing data. She has 5 years of experience in the field of computer vision and has worked on a complex driver safety solution to be deployed by many well-known car manufacturing companies.

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