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3D Machine Learning with PyTorch3D

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.

Intermediate

37 Lessons

Certificate of Completion

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.

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This course includes

1 Assessment

68 Playgrounds

5 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

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 ex...Show More

With the surge in XR, 3D printing, and the Metaverse, 3D is extremely popular. Likewise, investment in AI is growing exponential...Show More

TAKEAWAY SKILLS

Deep Learning

Python

PyTorch basics

Machine Learning

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

Understanding of fundamental computer graphics concepts

Course Content

Getting Started

4 Lessons

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

7 Lessons

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.

Rendering Overview

Ray Tracing vs. Rasterization

Barycentric Coordinates

Shading, Materials, and Textures

Lighting

Texture Estimation

Quiz Yourself on Rendering

Data Representations

6 Lessons

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

Voxels

Point Clouds

Meshes

Signed Distance Functions (SDF)

Fields

Quiz Yourself on Data Representations

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.

Heterogeneous Batching

Mesh Deformation

Efficient Perspective-n-Point (PnP)

Iterative Closest Point (ICP)

Graph Convolution

Voxel Branches and Volume Rendering

Positional Encoding

Quiz Yourself on Operations and Techniques

Key Models

3 Lessons

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

PointNet

Mesh R-CNN

Neural Radiance Fields (NeRF)

Final Assessment

Assessment

Conclusion

1 Lessons

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

Wrap Up

Appendix

1 Lessons

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

Appendix A: External Links

Course Author

Mason McGough

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