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 :mod:`kaolin.render.mesh` API in a Jupyter notebook:
examples/tutorial/dibr_tutorial.ipynb
In addition to the rendering API, the tutorial uses Omniverse Kaolin App Data Generator to create training data, :class:`kaolin.visualize.Timelapse` to write checkpoints, and Omniverse Kaolin App Training Visualizer to visualize them.