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3D Deep Learning with Python: Design and develop your computer vision model ...
US $31.79
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A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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eBay item number:286800776945
Item specifics
- Condition
- Release Year
- 2022
- Book Title
- 3D Deep Learning with Python: Design and develop your computer...
- ISBN
- 9781803247823
About this product
Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
1803247827
ISBN-13
9781803247823
eBay Product ID (ePID)
2329415985
Product Key Features
Number of Pages
236 Pages
Language
English
Publication Name
3D Deep Learning with Python : Design and Develop Your Computer Vision Model with 3D Data Using PyTorch3D and More
Subject
Machine Theory, Intelligence (Ai) & Semantics, Neural Networks
Publication Year
2022
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Length
3.6 in
Item Width
3 in
Additional Product Features
Intended Audience
Trade
Dewey Edition
23
Dewey Decimal
006.693
Table Of Content
Table of Contents 3D data file formats - ply and obj, 3D coordination systems, camera models Basic rendering concepts, basic PyTorch optimization, heterogeneous batching Fitting using deformable mesh models Differentiable rendering basic concepts Differentiable volume rendering NeRF - Neural Radiance Fields GIRAFFE Human body 3D fitting using SMPL models Synsin - end-to-end view synthesis from a single image Mesh RCNN
Synopsis
Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with ease 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. 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 Who this book is for: This book is for beginner to intermediate-level machine learning practitioners, data scientists, ML engineers, and DL engineers who are looking to become well-versed with computer vision techniques using 3D data., Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with ease 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. 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 Who this book is for This book is for beginner to intermediate-level machine learning practitioners, data scientists, ML engineers, and DL engineers who are looking to become well-versed with computer vision techniques using 3D data., This practical guide to 3D deep learning will help you learn everything you need to know about 3D computer vision models and how to incorporate them into your day-to-day work. The book covers top methods and frameworks to demonstrate how 3D data can be processed and help you gain the confidence to implement your own 3D deep learning models.
Item description from the seller
Seller feedback (198,995)
- r***s (114)- Feedback left by buyer.Past monthVerified purchasetook awhile to get, But book in very good condition.
- 5***5 (262)- Feedback left by buyer.Past monthVerified purchaseGood
Seller feedback (198,995)
- r***s (114)- Feedback left by buyer.Past monthVerified purchasetook awhile to get, But book in very good condition.
- 5***5 (262)- Feedback left by buyer.Past monthVerified purchaseGood