3D Deep Learning with Python: Design and develop your computer vision model with 3D data using PyTorch3D and more

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Key benefits

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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Redeem a companion digital copy on all Print orders

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Steven Fernandes Dec 12, 2022

The book is divided into three parts. The first part introduces 3D data processing basics. The authors introduce Ply, OBJ 3D data file formats and coding exercises for 3D rendering using PyTorch3D. The second part introduces 3D deep learning. This includes object pose detection by differentiable rendering, differentiable volumetric rendering and neural radiance fields. The final part introduces current deep learning using PyTorch 3D. This includes training GIRAFFEE model, understanding linear blend Skinning technique and SMPL model. The final 2 chapters includes end-t-end view synthesis with SynSin and Mesh R-CNN.

Amazon Verified review

Daniel Armstrong May 21, 2023

Overall it was a good book. It covered everything you would expect in a 3D deep learning book (point clouds, NeRFs, GANs, Mesh R-CNN, and that weird puffy cow) I haven't had any experience doing 3d deep learning, so this book was not an easy read for me. Given the complexity of the subject, its not that surprising. My advise to anyone reading it is to read it a few times, don't get wrapped up in concepts that you might not understand, they might become clearer when you come back to the subject. Personally I feel the subject of 3D deep learning is not quite ready for prime time, but with recent advancements with models like stable diffusion I think we are are going to see some amazing tools come out of this field.

Dror Nov 23, 2022

Deep learning has taken the world by storm and revolutionized entire fields such as computer vision and natural language processing. While there are already many good books on various aspects of deep learning, this book is a wonderful and rather unique resource for learning 3D deep learning.The book begins with covering basic concepts in 3D data processing, and progressively moves to cover essential tools and modern techniques for 3D computer vision and geometry. It provides a great introduction to PyTorch3D - an open-source library for 3D deep learning - and provides detailed and illuminating descriptions of state-of-the-art methods in 3D computer vision, such as NeRF, SynSin and Mesh R-CNN.While the book is rather short (about 220 pages), it is concise and serves as a great introduction to the fascinating world of 3D data processing with deep learning. I'd recommend it mostly to beginners and intermediate-level machine learning practitioners who are interested in mastering the different aspects of 3D deep learning. Note that it's also best to have access to a modern GPU to get the most out of this book, although this isn't a strict requirement.All in all, highly recommended!

Om S Feb 10, 2023

The book "3D Deep Learning with Python: Design and develop your computer vision model with 3D data using PyTorch3D" provides a comprehensive overview of the various techniques and models used in the field of 3D deep learning. The book covers a range of topics, including 3D data file formats (ply and obj), 3D coordination systems, camera models, and basic rendering concepts.The book also explores the basics of PyTorch optimization, heterogeneous batching, and the use of deformable mesh models for fitting. The differentiable rendering concept and differentiable volume rendering are also explained in detail. The book then goes on to cover advanced topics such as Neural Radiance Fields (NeRF), GIRAFFE, human body 3D fitting using SMPL models, end-to-end view synthesis from a single image (Synsin), and Mesh RCNN.Overall, this book is a comprehensive guide to the field of 3D deep learning and provides readers with a solid foundation in the various concepts, techniques, and models used in this field. The explanations are clear and well-structured, making it an accessible resource for both beginners and more experienced practitioners.

Simon Jul 01, 2023

I tried to download the PyTorch3d as usual there is problems for Mac uses trying to learn 3d learning!! Be warne3d

3D Deep Learning with Python: Design and develop your computer vision model with 3D data using PyTorch3D and more

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