| Description |
1 online resource |
| Summary |
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. |
| Contents |
Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- PART 1: 3D Data Processing Basics -- Chapter 1: Introducing 3D Data Processing -- Technical requirements -- Setting up a development environment -- 3D data representation -- Understanding point cloud representation -- Understanding mesh representation -- Understanding voxel representation -- 3D data file format -- Ply files -- 3D data file format -- OBJ files -- Understanding 3D coordination systems -- Understanding camera models -- Coding for camera models and coordination systems -- Summary |
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Chapter 2: Introducing 3D Computer Vision and Geometry -- Technical requirements -- Exploring the basic concepts of rendering, rasterization, and shading -- Understanding barycentric coordinates -- Light source models -- Understanding the Lambertian shading model -- Understanding the Phong lighting model -- Coding exercises for 3D rendering -- Using PyTorch3D heterogeneous batches and PyTorch optimizers -- A coding exercise for a heterogeneous mini-batch -- Understanding transformations and rotations -- A coding exercise for transformation and rotation -- Summary |
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PART 2: 3D Deep Learning Using PyTorch3D -- Chapter 3: Fitting Deformable Mesh Models to Raw Point Clouds -- Technical requirements -- Fitting meshes to point clouds -- the problem -- Formulating a deformable mesh fitting problem into an optimization problem -- Loss functions for regularization -- Mesh Laplacian smoothing loss -- Mesh normal consistency loss -- Mesh edge loss -- Implementing the mesh fitting with PyTorch3D -- The experiment of not using any regularization loss functions -- The experiment of using only the mesh edge loss -- Summary |
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Chapter 4: Learning Object Pose Detection and Tracking by Differentiable Rendering -- Technical requirements -- Why we want to have differentiable rendering -- How to make rendering differentiable -- What problems can be solved by using differentiable rendering -- The object pose estimation problem -- How it is coded -- An example of object pose estimation for both silhouette fitting and texture fitting -- Summary -- Chapter 5: Understanding Differentiable Volumetric Rendering -- Technical requirements -- Overview of volumetric rendering -- Understanding ray sampling -- Using volume sampling |
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Exploring the ray marcher -- Differentiable volumetric rendering -- Reconstructing 3D models from multi-view images -- Summary -- Chapter 6: Exploring Neural Radiance Fields (NeRF) -- Technical requirements -- Understanding NeRF -- What is a radiance field? -- Representing radiance fields with neural networks -- Training a NeRF model -- Understanding the NeRF model architecture -- Understanding volume rendering with radiance fields -- Projecting rays into the scene -- Accumulating the color of a ray -- Summary -- PART 3: State-of-the-art 3D Deep Learning Using PyTorch3D |
| Subject |
Three-dimensional imaging.
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Computer vision.
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Deep learning (Machine learning)
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Python (Computer program language)
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Imagerie tridimensionnelle. |
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Vision par ordinateur. |
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Apprentissage profond. |
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Python (Langage de programmation) |
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three-dimensional. |
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Computer vision |
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Deep learning (Machine learning) |
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Python (Computer program language) |
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Three-dimensional imaging |
| Added Author |
Hegde, Vishakh.
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Yolyan, Lilit.
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| Other Form: |
Print version: 9781803233680 |
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Print version: 1803247827 9781803247823 (OCoLC)1348922341 |
| ISBN |
9781803233680 (electronic bk.) |
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1803233680 (electronic bk.) |
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