Understand 3D data processing with rendering, PyTorch optimization, and heterogeneous batching
Implement differentiable rendering concepts with practical examples
Discover how you can ease your work with the latest 3D deep learning techniques using PyTorch3D
Description
With this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time.
Complete with step-by-step explanations of essential concepts and practical examples, this book lets you explore and gain a thorough understanding of state-of-the-art 3D deep learning. You’ll see how to use PyTorch3D for basic 3D mesh and point cloud data processing, including loading and saving ply and obj files, projecting 3D points into camera coordination using perspective camera models or orthographic camera models, rendering point clouds and meshes to images, and much more. As you implement some of the latest 3D deep learning algorithms, such as differential rendering, Nerf, synsin, and mesh RCNN, you’ll realize how coding for these deep learning models becomes easier using the PyTorch3D library.
By the end of this deep learning book, you’ll be ready to implement your own 3D deep learning models confidently.
What you will learn
Develop 3D computer vision models for interacting with the environment
Get to grips with 3D data handling with point clouds, meshes, ply, and obj file format
Work with 3D geometry, camera models, and coordination and convert between them
Understand concepts of rendering, shading, and more with ease
Implement differential rendering for many 3D deep learning models
Advanced state-of-the-art 3D deep learning models like Nerf, synsin, mesh RCNN
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David Farrugia is a seasoned data scientist and a Ph.D. candidate in AI at the University of Malta. David Farrugia has worked in diverse industries, including gaming, manufacturing, customer relationship management, affiliate marketing, and anti-fraud. He has an interest in exploring the intersection of business and academic research. He also believes that the emerging field of neuro-symbolic AI has the potential to revolutionize the way we approach AI and solve some of the most complex problems in the world.
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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