Key Features

• 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

Book 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.

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