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

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

  • Author:Lilit YolyanVishakh HegdeXudong Ma
  • Length: 236 pages
  • Edition: 1
  • Publisher: Packt Publishing
  • Publication Date: 2022-10-31
  • ISBN-10: 1803247827
  • ISBN-13: 9781803247823
  • Sales Rank: #442123 (See Top 100 Books)
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  • Buy Print:Buy from amazon



    Book Description

    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泰格豪雅 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 diffWEMPEerential rendering for many 3D deep learning models
    • Ad名士表vanced state-of-the-art 3D deep learning models like Nerf, synsin, mesh RCNN

    Who this bo普拉达ok 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.

    中文:

    书名:使用Python进行3D深度学习: 使用PyTorch3D等设计和开发具有3D数据的计算机视觉模型

    使用PyTorch3D和其他Python框架使用3博士音响D数据可视化和构建深度学习模型,轻松应对现实世界的应用挑战

    主要功能

    • 通过渲染,PyTorch优化和异构批处理了解3D数据处理
    • 用实际示例实现可微渲染概念
    • 了解如何使用PyTorch3D使用最新的3D深度学习技术轻松工作

    图书描述

    有了这个3D深度学习的动手指南,使用3D计算机视觉的开发人员将能够立即将他们的知识投入工作并启动并运行。

    本书通过对基本概念和实际示例的逐步解释,使您可以探索并全面了解最新的3D深度学习。您将看到如何使用PyTorch3D进行基本的3D网格和点云数据处理,包括加载和保存ply和obj文件,使用透视相机模型或正交相机模型将3D点投影到相机协调中,渲染点云和网格到图像,等等。当您实现一些最新的3D深度学习算法 (例如差分渲染,Nerf,synsin和mesh RCNN) 时,您将意识到如何使用PyTorch3D库对这些深度学习模型进行编码变得更加容易。

    在这本深度学习书的结尾,您将准备好自信地实现自己的3D深度学习模型。

    什么你会学到

    • 开发3D计算机视觉模型以与环境交互
    • 点云、网格、ply和obj文件格式的3D数据处理
    • 使用3D几何图形、相机模型和协调并转换它们
    • 轻松理解渲染、着色等概念
    • 实现多个三维深度学习模型的差分渲染
    • 先进的3D积家深度学习模型,如Nerf,synsin,mesh RCNN

    这本书是为奢侈腕表谁准备的

    本书面向初学者到中级机器学习从业者,数据科学家,ML工程师和DL工程师,他们希望精通使用3D数据的计算机视觉技术。

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