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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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.
With the surge in XR, 3D printing, and the Metaverse, 3D is extremely popular. Likewise, investment in AI is growing exponential...Show More
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
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TAKEAWAY SKILLS
Deep Learning
Python
PyTorch basics
Machine Learning
Content
37 Lessons5 Quizzes1 Assessment1 Challenge
1.
Getting Started
4 Lessons
Get familiar with 3D machine learning, PyTorch3D framework, and foundational machine learning concepts.
Dive into diverse 3D deep learning techniques, including batching, mesh deformation, PnP, ICP, graph convolution, voxel branches, and positional encoding.
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