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Not at all an expert in this, but I'm curious how this compares to non-NN solutions like https://research.nvidia.com/publication/efficient-sparse-vox...

Presumably it takes less memory, letting a more complex scene be transferred to the GPU more quickly, at the tradeoff of time spent training the model?

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Memory savings are the main potential gain, since you won't need as deep a tree. This is not the main issue with this tech atm.

Also, their distance output will be an estimate, it won't be exact, which will cause render time to underperform vs exact representations. Worse, the NN could return a value greater than actual distance, which will cause artifacts.

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