I don't like the "byte code" analogy for Gaussian splats. If they were like that, then we could apply compiler optimization and those sorts of math techniques to them. But they are Probability Distribution Functions with transforms, so the math tools we have to work with them are the similar to those in signal processing -- resampling, quantizing, estimating, etc.
In that model, we don't compile them, we train them; we don't run them, we sample/rasterize them.
This link came up on HN before and was a great refresher/expander on the math of Guassians which allow all this. [1].
Since Gaussians can be estimated, neural networks can model/generate them. Researchers are using this for 4D work and mesh extraction. The NNs run at lower frame rate informing the 3DGS running at interactive rates.
You are right that it is ephemeral and really a weird trick of the eye and we need new ways to edit/create it. Vectors/pixels have had a lot more time to grow tooling. People are working on it, just the toolbox is different. Very cool stuff will be coming up, I bet!
In that model, we don't compile them, we train them; we don't run them, we sample/rasterize them.
This link came up on HN before and was a great refresher/expander on the math of Guassians which allow all this. [1].
Since Gaussians can be estimated, neural networks can model/generate them. Researchers are using this for 4D work and mesh extraction. The NNs run at lower frame rate informing the 3DGS running at interactive rates.
You are right that it is ephemeral and really a weird trick of the eye and we need new ways to edit/create it. Vectors/pixels have had a lot more time to grow tooling. People are working on it, just the toolbox is different. Very cool stuff will be coming up, I bet!
[1] https://news.ycombinator.com/item?id=41912160 I've also re-learned Fourier transforms to appreciate similar concepts.