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You're reading from  3D Deep Learning with Python

Product typeBook
Published inOct 2022
PublisherPackt
ISBN-139781803247823
Edition1st Edition
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Authors (3):
Xudong Ma
Xudong Ma
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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.
Read more about Xudong Ma

Vishakh Hegde
Vishakh Hegde
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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.
Read more about Vishakh Hegde

Lilit Yolyan
Lilit Yolyan
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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.
Read more about Lilit Yolyan

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Understanding volume rendering with radiance fields

Volume rendering allows you to create a 2D projection of a 3D image or scene. In this section, we will learn about rendering a 3D scene from different viewpoints. For the purposes of this section, assume that the NeRF model is fully trained and that it accurately maps the input coordinates (x, y, z, d­­­x, dy, dz) to an output (r, g, b, σ). Here are the definitions of these input and output coordinates:

  • (x, y, z): A point in the 3D scene in the World Coordinates
  • (d­­­x, dy, dz): This is a unit vector that represents the direction along which we are viewing the point (x, y, z)
  • (r, g, b): This is the radiance value (or the emitted color) of the point (x, y, z)
  • σ: The volume density at the point (x, y, z)

In the previous chapter, you came to understand the concepts underlying volumetric rendering. You used the technique of ray sampling to get volume densities and colors...

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3D Deep Learning with Python
Published in: Oct 2022Publisher: PacktISBN-13: 9781803247823

Authors (3)

author image
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.
Read more about Xudong Ma

author image
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.
Read more about Vishakh Hegde

author image
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.
Read more about Lilit Yolyan