3D Deep Learning with Python: Design and develop your computer vision model with

Description: 3D Deep Learning with Python by Xudong Ma, Vishakh Hegde, Lilit Yolyan 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with easeKey FeaturesUnderstand 3D data processing with rendering, PyTorch optimization, and heterogeneous batchingImplement differentiable rendering concepts with practical examplesDiscover how you can ease your work with the latest 3D deep learning techniques using PyTorch3DBook DescriptionWith 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. Youll 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, youll realize how coding for these deep learning models becomes easier using the PyTorch3D library.By the end of this deep learning book, youll be ready to implement your own 3D deep learning models confidently.What you will learnDevelop 3D computer vision models for interacting with the environmentGet to grips with 3D data handling with point clouds, meshes, ply, and obj file formatWork with 3D geometry, camera models, and coordination and convert between themUnderstand concepts of rendering, shading, and more with easeImplement differential rendering for many 3D deep learning modelsAdvanced state-of-the-art 3D deep learning models like Nerf, synsin, mesh RCNNWho this book is forThis 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. Author Biography Xudong Ma is a Staff Machine Learning engineer with Grabango Inc. at Berkeley California. He was a Senior Machine Learning Engineer at Facebook(Meta) Oculus and worked closely with the 3D PyTorch Team on 3D facial tracking projects. He has many years of experience working on computer vision, machine learning and deep learning. He holds a Ph.D. in Electrical and Computer Engineering. Vishakh Hegde is a Machine Learning and Computer Vision researcher. He has over 7 years of experience in this field during which he has authored multiple well cited research papers and published patents. He holds a masters from Stanford University specializing in applied mathematics and machine learning, and a BS and MS in Physics from IIT Madras. He previously worked at Schlumberger and Matroid. He is a Senior Applied Scientist at Ambient.ai, where he helped build their weapon detection system which is deployed at several Global Fortune 500 companies. He is now leveraging his expertise and passion to solve business challenges to build a technology startup in Silicon Valley. You can learn more about him on his personal website. Lilit Yolyan is a machine learning researcher working on her Ph.D. at YSU. Her research focuses on building computer vision solutions for smart cities using remote sensing data. She has 5 years of experience in the field of computer vision and has worked on a complex driver safety solution to be deployed by many well-known car manufacturing companies. Table of Contents Table of Contents3D data file formats - ply and obj, 3D coordination systems, camera modelsBasic rendering concepts, basic PyTorch optimization, heterogeneous batchingFitting using deformable mesh modelsDifferentiable rendering basic conceptsDifferentiable volume renderingNeRF - Neural Radiance FieldsGIRAFFEHuman body 3D fitting using SMPL modelsSynsin - end-to-end view synthesis from a single imageMesh RCNN Details ISBN1803247827 Author Lilit Yolyan Short Title 3D Deep Learning with Python Language English Year 2022 ISBN-10 1803247827 ISBN-13 9781803247823 Format Paperback Publisher Packt Publishing Limited Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom Pages 236 Subtitle Design and develop your computer vision model with 3D data using PyTorch3D and more Publication Date 2022-10-31 AU Release Date 2022-10-31 NZ Release Date 2022-10-31 UK Release Date 2022-10-31 DEWEY 006.37 Audience General We've got this At The Nile, if you're looking for it, we've got it. 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