October 2022
Intermediate to advanced
236 pages
5h 13m
English
Overview
Dive into the world of 3D deep learning with this practical guide that focuses on using Python and PyTorch3D for computer vision tasks. This book walks you through key concepts such as 3D data handling, differentiation rendering, and cutting-edge 3D learning models. By the end, you'll have the skills to confidently build your own 3D deep learning systems.
What this Book will help me do
- Learn how to process and handle 3D data formats like ply and obj for real-world applications.
- Gain expertise with 3D coordination systems, camera models, and rendering techniques for accurate image synthesis.
- Understand and apply differential rendering using PyTorch3D for advanced deep learning applications.
- Master the implementation of neural rendering methods like NeRF and synsin for innovative image generation.
- Develop and deploy state-of-the-art 3D deep learning solutions including mesh RCNN and SMPL models.
Author(s)
Xudong Ma, None Farrugia, Vishakh Hegde, and Lilit Yolyan are experts in artificial intelligence and computer vision with hands-on experience in building and deploying 3D deep learning solutions. Their collaborative efforts bring together a wealth of practical examples and deep theoretical knowledge. With a passion for teaching, they aim to make complex 3D learning concepts approachable for practitioners of all levels.
Who is it for?
This book is tailored for machine learning engineers, data scientists, and developers eager to delve into the intricacies of 3D computer vision. If you have a basic understanding of Python programming and machine learning principles and wish to expand your expertise into handling 3D data and advanced rendering techniques, this guide is for you. Ready to elevate your skills and work on groundbreaking applications? This book is your next step.
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