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3D Machine Learning with PyTorch3D

Name: 3D Machine Learning with PyTorch3D
Price: 59 USD
Author: Mason McGough

Gain insights into PyTorch3D's role in XR and AI. Delve into camera parameters, rendering pipelines, and 3D data formats. Learn about PointNet, Mesh R-CNN, and Neural Radiance Fields.

5.0

37 lessons

1 project

Created by Mason McGough

What's included

37 lessons across 8 modules
5 quizzes with instant feedback
1 project in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Understanding of fundamental computer graphics concepts
Working knowledge of computer vision and machine learning techniques for graphics
Ability to apply machine learning techniques to 3D data
Familiarity with techniques to transform, render, and manipulate 3D data
Hands-on experience building machine learning applications using PyTorch3D

Learning Roadmap

37 Lessons5 Quizzes1 Assessment1 Challenge

Getting Started

Get familiar with 3D machine learning, PyTorch3D framework, and foundational machine learning concepts.

Introduction to 3D Machine Learning

The PyTorch3D Framework

Review of Machine Learning

Quiz Yourself on ML Basics

Cameras and Projection

Walk through camera models, coordinate transformations, projection methods, and 3D-2D image rendering in PyTorch3D.

Image Formation and the Thin Lens Equation

Coordinate Spaces

Camera Parameters (Intrinsic and Extrinsic)

Homogeneous Coordinates

Projection Models

Transformations

Quiz Yourself on Cameras and Projections

Rendering

7 Lessons

Work your way through rendering techniques, ray tracing, shading, lighting, and texture estimation.

Data Representations

6 Lessons

Grasp the fundamentals of 3D data representations in machine learning, including voxels, point clouds, meshes, SDFs, and fields.

Operations and Techniques

8 Lessons

Dive into diverse 3D deep learning techniques, including batching, mesh deformation, PnP, ICP, graph convolution, voxel branches, and positional encoding.

Key Models

3 Lessons

Tackle groundbreaking models for 3D data processing, shape prediction, and novel view synthesis.

Final Assessment

Assessment

Conclusion

Build on essential knowledge and skills in 3D machine learning with PyTorch3D.

Wrap Up

Appendix

Learn how to use valuable external resources on 3D machine learning and related tools.

Appendix A: External Links

Link-Based Classification Using Graph Neural Networks

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

With the surge in XR, 3D printing, and the Metaverse, 3D is extremely popular. Likewise, investment in AI is growing exponentially in most industrial sectors. PyTorch3D is the leading machine learning framework that bridges the gap between AI and 3D. It is essential for tools that generate 3D models from single images and 3D rooms from text prompts. After an introduction to PyTorch3D, you’ll learn how 2D images are formed from a 3D world, covering camera parameters, projection models, etc. Next, you’ll explore the rendering pipeline and how 3D data is used to generate images. You’ll explore the differences between 3D formats, and look at machine learning methods such as heterogeneous batching, graph convolution, etc. Lastly, you’ll learn foundational models of 3D machine learning—PointNet, Mesh R-CNN, and Neural Radiance Fields. By the time you’re done, you’ll have a solid understanding of 3D concepts, how to process various types of 3D data with PyTorch, and how to build 3D machine learning systems.

ABOUT THE AUTHOR

Mason McGough

Machine Learning and Computer Vision engineer on the threshold between the physical and digital worlds.

Learn more about Mason

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