There's an interesting recent Siggraph paper on scaling up Gaussian splatting in a very different way, which offers some interesting benefits; Gaussian point splatting.
The biggest update is adding alpha compensation (alpha > 1). With this in place we can addresses the gaps that appear after performing many rounds of decimation.
"Hubris: Redux" is a pretty good description of my entire career.
The high points, anyway. The parts worth remembering.
Something I am taking away from this, is every optimization he makes here, takes away from the technologies supporting the overall dynamism of the scene, "dynamism" in terms of things interacting with things. The long solution might well be a very tightly described engine of "dynamism" - data formats, algorithms - that then feeds an optimized splat renderer, millisecond by millisecond.
https://trianglesplatting.github.io/
* Video: https://www.youtube.com/watch?v=2AWv_nIfp-U
* Paper: https://jorisar.nl/gaussian_point_splatting/gaussian_point_s...
* Repo: https://github.com/JorisAR/gaussian-point-splatting
With the simple equipements, the result is spectacle.
https://superspl.at/scene/c67edb74
It has 50m splats, captured by XGRIDS K2 camera in roughly 1 hour of walking around.
https://github.com/playcanvas/splat-transform/issues/362
The high points, anyway. The parts worth remembering.
Something I am taking away from this, is every optimization he makes here, takes away from the technologies supporting the overall dynamism of the scene, "dynamism" in terms of things interacting with things. The long solution might well be a very tightly described engine of "dynamism" - data formats, algorithms - that then feeds an optimized splat renderer, millisecond by millisecond.
Gaussian splats are surprisingly easy to make: https://superspl.at/scene/82eb1744 (I used a thermal camera - so I could also project the IR onto it too)https://superspl.at/scene/f9672a5e