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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?

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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.

Except that no view-dependent effects that would benefit multi-view consistency are present in your splats.

So yes, it's very much like the RGB-D visualizations from 10 years ago, just with splats instead of points.

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.

[1] https://imgur.com/a/2yA7eMU

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