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.

3D Machine Learning with PyTorch3D

Learn about the iterative closest point algorithm and how to use it in PyTorch3D.

Getting Started

Cameras and Projection

Rendering

Data Representations

Operations and Techniques

Key Models

Final Assessment

Conclusion

Appendix

Iterative Closest Point (ICP)

Overview