Gaussian Splatting is pretty cool!

SIGGRAPH 2023 just had a paper “3D Gaussian Splatting for Real-Time Radiance Field Rendering” by Kerbl, Kopanas, Leimkühler, Drettakis, and it looks pretty cool! Check out their website, source code repository, data sets and so on (I should note that it is really, really good to see full source and full data sets being released. Way to go!).

I’ve decided to try to implement the realtime visualization part (i.e. the one that takes already-produced gaussian splat “model” file) in Unity. As well as maybe play around with looking at whether the data sizes could be made smaller (maybe use some of the learnings from float compression series too?).

What’s a few million badly rendered boxes among friends, anyway?

For the impatient: I got something working over at aras-p/UnityGaussianSplatting, and will tinker with things there some more.

Meanwhile, some quick random thoughts on this Gaussian Splatting thing.

What are these Gaussian Splats?

Watch the paper video and read the paper, they are pretty good!

I have seen quite many 3rd party explanations of the concept at this point, and some of them, uhh, get a thing or two wrong about it :)

  • This is not a NeRF (Neural Radiance Field)! There is absolutely nothing “neural” about it.
  • It is not somehow “fast, because it uses GPU rasterization hardware”. The official implementation does not use the rasterization pipeline at all; it is 100% done with CUDA. In fact, it is fast because it does not use the fixed function rasterization, as we’ll see below.


Gaussian Splats are, basically, “a bunch of blobs in space”. Instead of representing a 3D scene as polygonal meshes, or voxels, or distance fields, it represents it as (millions of) particles:

  • Each particle (“a 3D Gaussian”) has position, rotation and a non-uniform scale in 3D space.
  • Each particle also has an opacity, as well as color (actually, not a single color, but rather 3rd order Spherical Harmonics coefficients - meaning “the color” can change depending on the view direction).
  • For rendering, the particles are rendered (“splatted”) as 2D Gaussians in screen space, i.e. they are not rendered as scaled elongated spheres, actually! More on this below.

And that’s it. The “Gaussian Splatting” scene representation is just that, a whole bunch of scaled and colored blobs in space. The genius part of the paper is several things:

  • They found a way how to create these millions of blobs that would represent a scene depicted by a bunch of photos. This is using gradient descent and “differentiable rendering” and all the other things that are way over my head. This feels like the major contribution, like maybe previously people assumed that in order for gradient descent optimizer to work nicely, you need to use a continuous or connected scene representation (vs “just a bunch of blobs”), and this paper proved that wrong? Anyway, I don’t understand this area, so I won’t talk about it more :)
  • They have developed a fast way to render all these millions of scaled particles. This by itself is not particularly ground breaking IMHO, various people have noticed that using something like a tile-based “software, but on GPU” rasterizer is a good way to do this.
  • They have combined existing established approaches (like gaussian splatting, and spherical harmonics) in a nice way.
  • And finally, they have resisted the temptation to do “neural” anything ;)

Previous Building Blocks

The Gaussian Splatting seems to be invented around year 2001-2002, see for example “EWA Splatting” paper by Zwicker, Pfister, Van Baar, Gross. There they have scaled and oriented “blobs” in space, calculate how would they project onto screen, and then do the actual “blob shape” (a “Gaussian”) in 2D, in screen-space. A bunch of signal processing, sampling, aliasing etc. math presumably supports doing it that way.

Speaking of ellipsoids, Ecstatica game from 1994 had a fairly unique ellipsoid-based renderer.

Spherical Harmonics (a way to represent a function over a surface of a sphere) have been around for several hundred years in physics, but really were popularized in computer graphics around 2000 by Ravi Ramamoorthi and Peter-Pike Sloan. But actually, a 1984 “Ray tracing volume densities” paper by Kajiya & Von Herzen might be the first use of them in graphics. A nice summary of various things related to SH is at Patapom’s page.

Point-Based Rendering in various forms has been around for a long time, e.g. particle systems were used since “forever” (but typically used for vfx / non-solid phenomena). “The Use of Points as a Display Primitive” is from 1985. “Surfels” paper is from 2000.

Something closer to my heart, a whole bunch of demoscene demos are using non-traditional rendering approaches. Fairlight & CNCD have several notable examples, e.g. Agenda Circling Forth (2010), Ceasefire (all falls down..) (2010), Number One / Another One (2018):

Real-time VFX tools like Notch have pretty extensive features for creating, simulating and displaying point/blob based “things”.

Ideas of representing images or scenes with a bunch of “primitive shapes”, as well as tools to generate those, have been around too. E.g. fogleman/primitive (2016) is nice.

Media Molecule “Dreams” has a splat-based renderer (I think the shipped version is not purely splat-based but a combination of several techniques). Check out the most excellent “Learning from Failure” talk by Alex Evans: at SIGGRAPH 2015 (splats start at slide 109) or video from Umbra Ignite 2015 (splats start at 22:34).

Tiled Rasterization for particles has been around at least since 2014 (“Holy smoke! Faster Particle Rendering using Direct Compute” by Gareth Thomas). And the idea that dividing screen into tiles, doing a bunch of things “inside the tile” thus cutting on memory traffic, is how entire mobile GPU space operates, and has been operating since “forever”, tracing back to first PowerVR designs (1996) and even Pixel Planes 5 from 1989.

This is all great! Taking existing, developed, solid building blocks and combining them in a novel way is excellent work.

My Toy Playground

My current implementation (of just the visualizer of Gaussian Splat models) for Unity is over at github: aras-p/UnityGaussianSplatting. Current state is “it kinda works, but it is not fast”:

  • The rendering does not look horrible, but does not exactly match official implementation. Here is official vs my rendering of the same scene. Official one has more small detail, and lighting is slightly different Fixed!
  • Performance is not great. The scene above renders on NVIDIA RTX 3080 Ti at 1200x800 in 7.40ms (135FPS) in the official viewer, whereas my attempt is 23.8ms (42FPS) currently, i.e. 4x slower. For sorting I’m using some fairly simple GPU bitonic sort (official impl uses CUDA radix sort which is based on OneSweep algorithm). Rasterization in their case is tile-based and written in CUDA, whereas I’m “just” using regular GPU rasterization pipeline and rendering each splat as a screenspace quad.
    • On the plus side, my code is all regular HLSL within Unity, which means it also happens to work on e.g. Mac just fine. The scene above on Apple M1 Max renders in 108ms (9FPS) though :/
    • My implementation seems to use 2x less GPU memory right now too (official viewer: 4.8GB, mine: 2.2GB and that’s including whatever Unity editor takes).

So all of that could be improved and optimized quite a bit!

One thing I haven’t seen talked much about, by everyone super excited about Gaussian Splats, is data size and memory usage. Yeah, rendering is nice, but this bicycle scene above is 1.5GB of data on-disk, and then at runtime it needs some more (for sorting, tile based rendering etc.). That scene is six million blobs in space, with each of them taking about 250 bytes. There has to be some way to make that smaller! Actually the Dreams talk above has some neat ideas.

Maybe I should play around with that. Someday!