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.

pytorch3d-image.tar.gz

pytorch3d

intro-to-3d-deep-learning

camera-parameters

transformations

differentiable-rendering

barycentric-coordinates

learn-texture

mesh-deformation

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

Given `meshes`, a Meshes object containing two meshes with 40 vertices, what output shape will the `meshes.verts_packed().shape` command produce?

Given <code>meshes</code>, a Meshes object containing two meshes with 40 vertices, what output shape will the <code>meshes.verts_packed().shape</code> command produce?

Which type of loss is *not* used for mesh deformation?

Which type of loss is not used for mesh deformation?

What is the minimum number of points to find a solution for the perspective-n-point algorithm?

What is the minimum number of points to find a solution for the perspective-n-point algorithm?

ICP iteratively pairs points in a point cloud by minimum distance to one another to construct a 3D mesh.

ICP iteratively pairs points between two point clouds and estimates a transformation  to align one point cloud to the other.

ICP solves a system of equations of image points to estimate all six degrees of freedom of a calibrated camera.

Which statement best describes the iterative closest point (ICP) algorithm?

Which statement best describes the iterative closest point (ICP) algorithm?

**(True or False)** The iterative closest point (ICP) algorithm requires two point clouds with the same number of points.

(True or False) The iterative closest point (ICP) algorithm requires two point clouds with the same number of points.

`gconv(meshes.verts_packed(), meshes.edges_packed())`

`gconv(meshes.verts_padded(), meshes.edges_padded())`

If `gconv` is an object instance of `GraphConv` and `meshes` is an object instance of `Meshes`, which statement  is a valid forward pass?

If <code>gconv</code> is an object instance of <code>GraphConv</code> and <code>meshes</code> is an object instance of <code>Meshes</code>, which statement is a valid forward pass?

Quiz

Quiz

Test your knowledge of heterogeneous batching, mesh deformation, graph convolution, and other operations and techniques.

Getting Started

Cameras and Projection

Rendering

Data Representations

Operations and Techniques

Key Models

Final Assessment

Conclusion

Appendix

Quiz Yourself on Operations and Techniques