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

```
grid = torch.full([n_batch, depth, height, width, 1], val)
```

```
p = torch.linspace(0, 2 * torch.pi, 32)
```

```
points = torch.randn([n_batch, 3])
```

```
vol = Volumes(densities, features)
```

**(Select all that apply.)** Which code snippets create a valid voxel grid?


(Select all that apply.) Which code snippets create a valid voxel grid?

**(True or False)**
Point clouds do not uniquely describe the topology of a surface, even if they include normal vectors.

(True or False)
Point clouds do not uniquely describe the topology of a surface, even if they include normal vectors.

**(Select all that apply.)** Which  vertex, face, or edge definitions for a 3D mesh are invalid?

(Select all that apply.) Which vertex, face, or edge definitions for a 3D mesh are invalid?

When rendering an SDF, how do we test for intersections with a ray?

When rendering an SDF, how do we test for intersections with a ray?

**(Select all that apply.)** Which types of data can be considered a field?


(Select all that apply.) Which types of data can be considered a field?

Quiz

Quiz

Test your knowledge of voxels, point clouds, meshes, SDFs, and fields.

Getting Started

Cameras and Projection

Rendering

Data Representations

Operations and Techniques

Key Models

Final Assessment

Conclusion

Appendix

Quiz Yourself on Data Representations