Comment by _verandaguy 3 days ago
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?
So, is there some amount of gradient-based optimization going on here? I see RGBD input, transmission, RGBD output. But, other than multi-camera registration, it's difficult to determine what processing took place between input and transmission. What makes this different from RGBD camera visualizations from 10 years ago?
There is no gradient-based optimization. It's (RGBD input, Current Camera Pose) -> Neural Net -> Gaussian Splat output.
I'm not aware of other live RGBD visualizations except for direct pointcloud rendering. Compared to pointclouds, splats are better able to render textures, view-dependent effects, and occlusions.
Here is an example of a view dependent effect produced by LiveSplat [1]. Look closely at the wooden chair handle as the view changes.
I'll concede that ten years ago, someone could have done this. But no one did, as far as I know.
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.
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 :)