Differentiable rendering can be used to optimize the underlying 3D properties, like geometry and lighting, by backpropagating gradients from the loss in the image space. We provide an end-to-end tutorial for using the
kaolin.render.mesh API in a Jupyter notebook:
In addition to the rendering API, the tutorial uses Omniverse Kaolin App Data Generator to create training data,
kaolin.visualize.Timelapse to write checkpoints, and
Omniverse Kaolin App Training Visualizer to visualize them.