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142 points markisus | 1 comments | | HN request time: 0.303s | source

LiveSplat is a system for turning RGBD camera streams into Gaussian splat scenes in real-time. The system works by passing all the RGBD frames into a feed forward neural net that outputs the current scene as Gaussian splats. These splats are then rendered in real-time. I've put together a demo video at the link above.
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echelon ◴[] No.43995052[source]
OP, this is incredible. I worry that people might see a "glitchy 3D video" and might not understand the significance of this.

This is getting unreal. They're becoming fast and high fidelity. Once we get better editing capabilities and can shape the Gaussian fields, this will become the prevailing means of creating and distributing media.

Turning any source into something 4D volumetric that you can easily mold as clay, relight, reshape. A fully interactable and playable 4D canvas.

Imagine if the work being done with diffusion models could read and write from Gaussian fields instead of just pixels. It could look like anything: real life, Ghibli, Pixar, whatever.

I can't imagine where this tech will be in five years.

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_verandaguy ◴[] No.43995142[source]
I know enough about 3D rendering to know that Gaussian splatting's one of the Big New Things in high-performance rendering, so I understand that this is a big deal -- but I can't quantify why, or how big a deal it is.

Could you or someone else wise in the ways of graphics give me a layperson's rundown of how this works, why it's considered so important, and what the technical challenges are given that an RGB+D(epth?) stream is the input?

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markisus ◴[] No.43995241[source]
Gaussian Splatting allows you to create a photorealistic representation of an environment from just a collection of images. Philosophically, this is a form of geometric scene understanding from raw pixels, which has been a holy grail of computer vision since the beginning.

Usually creating a Gaussian splat representation takes a long time and uses an iterative gradient-based optimization procedure. Using RGBD helps me sidestep this optimization, as much of the geometry is already present in the depth channel and so it enables the real-time aspect of my technique.

When you say "big deal", I imagine you are also asking about business or societal implications. I can't really speak on those, but I'm open to licensing this IP to any companies which know about big business applications :)

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1. _verandaguy ◴[] No.43998347[source]
Thanks! That makes a lot of sense, I might dig into this after work some more.

By "big deal," I meant more for people specializing around computer graphics, computer vision, or even narrower subfields of either of those two -- a big deal from an academic interest perspective.

Sure, this might also have implications in society and business, but I'm a nerd, and I appreciate a good nerding out over something cool, niche, and technically impressive.