AI-powered learning
Save this course
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.
5.0
37 Lessons
9h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
1.
Getting Started
Getting Started
Get familiar with 3D machine learning, PyTorch3D framework, and foundational machine learning concepts.
2.
Cameras and Projection
Cameras and Projection
Walk through camera models, coordinate transformations, projection methods, and 3D-2D image rendering in PyTorch3D.
3.
Rendering
Rendering
7 Lessons
7 Lessons
Work your way through rendering techniques, ray tracing, shading, lighting, and texture estimation.
4.
Data Representations
Data Representations
6 Lessons
6 Lessons
Grasp the fundamentals of 3D data representations in machine learning, including voxels, point clouds, meshes, SDFs, and fields.
5.
Operations and Techniques
Operations and Techniques
8 Lessons
8 Lessons
Dive into diverse 3D deep learning techniques, including batching, mesh deformation, PnP, ICP, graph convolution, voxel branches, and positional encoding.
6.
Key Models
Key Models
3 Lessons
3 Lessons
Tackle groundbreaking models for 3D data processing, shape prediction, and novel view synthesis.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
Developed by MAANG Engineers
ABOUT 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.
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
Built for 10x Developers
No Passive Learning
Learn by building with project-based lessons and in-browser code editor
Personalized Roadmaps
The platform adapts to your strengths & skills gaps as you go
Future-proof Your Career
Get hands-on with in-demand skills
AI Code Mentor
Write better code with AI feedback, smart debugging, and "Ask AI"
MAANG+ Interview Prep
AI Mock Interviews simulate every technical loop at top companies
Free Resources
