3D Deep Learning with Python

Visualize and build Deep Learning models with 3D data using Pytorch3D and more to solve challenging real-world applications with ease

Key Features

• Learn 3D data handling with point clouds, meshes, ply, and obj file format
• Learn 3D geometry, camera models, and coordination and convert between them
• Understanding 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

Book Description

Developers working with 3D computer vision will be able to put their knowledge to work with this practical guide to 3D deep learning. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time.

Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin by exploring the state-of-the-art 3D deep learning

You will learn basic 3D mesh and point cloud data processing using PyTorch3D, such as, loading and saving ply and obj files, projecting 3D points into camera coordinations by perspective camera models or orthographic camera models, rendering point clouds and meshes to images etc. You will also learn how to implement some state-of-the-art 3D deep learning algorithms, such as, differential rendering, Nerf, synsin, mesh RCNN etc, because coding for these deep learning models becomes easier by using the PyTorch3D library.

By the end of this book, you will be able to implement your own 3D deep learning models.

What you will learn

• Learn 3D data processing with rendering, PyTorch optimization, and heterogeneous batching
• Learn differentiable rendering concepts with practical examples
• Latest 3D deep learning techniques using PyTorch3D to ease your work

Who This Book Is For

This book is for beginners to intermediate-level machine learning practitioners, Data Scientists, ML engineers, DL engineers who are looking to get well-versed with computer vision techniques using 3D data.

Table of Contents

1. 3D data file formats - ply and obj, 3D coordination systems, camera models
2. Basic rendering concepts, basic PyTorch optimization, heterogeneous batching
3. Fitting using deformable mesh models
4. Differentiable rendering basic concepts
5. Differentiable volume rendering
6. NeRF - Neural Radiance Fields
7. GIRAFFE
8. Human body 3D fitting using SMPL models
9. Synsin - end-to-end view synthesis from a single image
10. Mesh RCNN