It uses diff-gaussian-rasterization from the original gaussian splatting implementation (which, is a linked submodule on the git, so if you are trying to git clone that dependency remember to use --recursive to actually download it).
But that is written in mostly pure CUDA.
That part is just used to display the resulting gaussian splatt'd model, and there have been other cross-platform implementations to render splats – there was even that web demo a few weeks ago, that was using WebGL [0] – and if that was used as a display output in place of the original implementation there is no reason people couldn't use this on non-Nvidia hardware, I think.
edit: also device=cuda is hardcoded in the torch portions of the training code (sigh!). This doesn't have to be the case. pytorch could push this onto mps (metal) probably just fine.
Comments
Oof, the dependency tree on this.
It uses diff-gaussian-rasterization from the original gaussian splatting implementation (which, is a linked submodule on the git, so if you are trying to git clone that dependency remember to use --recursive to actually download it).
But that is written in mostly pure CUDA.
That part is just used to display the resulting gaussian splatt'd model, and there have been other cross-platform implementations to render splats – there was even that web demo a few weeks ago, that was using WebGL [0] – and if that was used as a display output in place of the original implementation there is no reason people couldn't use this on non-Nvidia hardware, I think.
edit: also device=cuda is hardcoded in the torch portions of the training code (sigh!). This doesn't have to be the case. pytorch could push this onto mps (metal) probably just fine.
[0] https://github.com/antimatter15/splat?tab=readme-ov-file