It will be interesting to see the first AAA game that uses these methods instead of rendering a 3D world. Even if made from CGI worlds, it would be a very interesting approach and with somewhat predictable performances.
Reminds me of Ecstatica [1], a 1994 game that had intense visuals with a very odd/different rendering engine made of 3D ellipsoids; in a way really crude splats in gouraud shading.
Many years ago there was a game called Casebook[1], a small little detective game where you investigated rooms for clues. But unlike similar FMV games where you jumped from point to point, it had photorealistic environments that could be smoothly walked around in, much like later lightfield or gaussian splatting experiments.
The contributions of 3DGS lie in how fast you can make them in modern GPU hardware (tiling + sorting with threads), and how to make the pipeline differentiable so that you can fit the Gaussian splats with photogrammetry data. Similar to the history of deep learning, it became technically feasible once the GPU hardware was powerful enough.
This is "rendering a 3D world". It's basically the exact same techniques that traditional rendering uses, just with a different primitive that's not triangles. Everything else pretty much carries over.
If you mean the technique of splatting specifically, Dreams for PS4 [1] is prior art.
If you mean pre-rendering, there's Myst and games like the original FF7 for PS1.
Can someone point to a resource/tutorial for learning point splatting (the 90s rendering technique)? Gaussian Splatting has completely over taken the search results, and the original technique is now near impossible to find.
Really nice idea for 3DGS rendering - though the main problem is the noise (an unfortunate issue for all Monte-Carlo based methods).
I think future papers would probably continue improving on this method and focus on how to sample the points more efficiently while being unbiased (similar to how ray-tracing solved their performance issues). Or maybe... we can just add a deep-learning based denoiser and call it a day!
I love this site design. It uses the entire width of the monitor rather than a slender column of pixels down the middle with large blocks of unused space on either side, with a font for my old man eyes.
Cannot find anything related to Monte Carlo methods in the source code. I thought Spark implemented a conventional 3DGS pipeline with LoD optimizations (And it seems they do the sorting on the CPU using Rust/WebAssembly because of WebGL limitations)
It's not new - that was sort of my point with my other comment.
At least if it's progressive (so refines and resolves over time), this has been done with pointclouds in the VFX industry in GPU shaders for years in terms of stochastically drawing different points so eventually the whole point set gets rasterised to a fidelity threshold.
ookay, thanks for the clarification! So, the interesting part here seems to be the 3DGS-specific opacity correction and GPU workload mapping. Am I wrong?
Sorting the gaussians is the compute heavy part in gaussian splatting. So, Im guessing this will give only marginal improvement in terms rendering speed.
Their point splatting method is orthogonal to level-of-detail rendering (they reference a few papers which try to do this), so both point splatting and LoD could be combined in the future for an even greater performance gain during rendering. They already implement occlusion and frustum culling.
Point splatting does introduce a lot of noise though, and their denoiser introduces ghosting, but they say a more sophisticated denoiser would give considerably better quality.
Really?! What OSs can handle that many native threads?
Also, this seems quite similar to stochastic progressive drawing of pointclouds for realtime that has been done for > 15 years in the VFX industry with GPU shaders in a tiled/bucketed fashion, unless this isn't progressive maybe? (The fact it's been accepted for Siggraph likely indicates it's slightly different).
Reminds me of Ecstatica [1], a 1994 game that had intense visuals with a very odd/different rendering engine made of 3D ellipsoids; in a way really crude splats in gouraud shading.
[1] https://ecstatica.fandom.com/wiki/Ecstatica
[1] https://www.youtube.com/watch?v=o-VAaC5BgVE
The contributions of 3DGS lie in how fast you can make them in modern GPU hardware (tiling + sorting with threads), and how to make the pipeline differentiable so that you can fit the Gaussian splats with photogrammetry data. Similar to the history of deep learning, it became technically feasible once the GPU hardware was powerful enough.
If you mean the technique of splatting specifically, Dreams for PS4 [1] is prior art.
If you mean pre-rendering, there's Myst and games like the original FF7 for PS1.
[1] https://en.wikipedia.org/wiki/Dreams_(video_game)
I think future papers would probably continue improving on this method and focus on how to sample the points more efficiently while being unbiased (similar to how ray-tracing solved their performance issues). Or maybe... we can just add a deep-learning based denoiser and call it a day!
<3
Umm on my machine it has 560px margin on both sides with the content being only 474px sliver in the middle?
https://github.com/sparkjsdev/spark
At least if it's progressive (so refines and resolves over time), this has been done with pointclouds in the VFX industry in GPU shaders for years in terms of stochastically drawing different points so eventually the whole point set gets rasterised to a fidelity threshold.
Or the per-pixel coord atomic I guess?
Point splatting does introduce a lot of noise though, and their denoiser introduces ghosting, but they say a more sophisticated denoiser would give considerably better quality.
Really?! What OSs can handle that many native threads?
Also, this seems quite similar to stochastic progressive drawing of pointclouds for realtime that has been done for > 15 years in the VFX industry with GPU shaders in a tiled/bucketed fashion, unless this isn't progressive maybe? (The fact it's been accepted for Siggraph likely indicates it's slightly different).
Future proofing I guess...