The PyTorch3D section was genuinely useful for me. I've been doing 2D ML work for a while but hadn't explored 3D deep learning — didn't even know PyTorch3D existed until this tutorial.

What worked well was the progressive complexity. Starting with basic mesh rendering before jumping into differentiable rendering made the concepts click. The voxel-to-mesh conversion examples were particularly clear.

If anything, I'd love to see a follow-up covering point cloud handling, since that seems to be a major use case based on the docs I'm now digging through.

Thanks for writing this — triggered a weekend deep-dive I probably wouldn't have started otherwise.