I look forward to reading this in closer detail, but it looks like they solve an inverse problem to recover a ground truth set of voxels (from a large set of 2d images with known camera parameters), which is underconstrained. Neat to me that it works w/o using dense optical flow to recover the structure -- I wouldn't have thought that would converge.
Love this a whole heck of a lot more than NeRF, or any other "lol lets just throw a huge network at it" approach.
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I look forward to reading this in closer detail, but it looks like they solve an inverse problem to recover a ground truth set of voxels (from a large set of 2d images with known camera parameters), which is underconstrained. Neat to me that it works w/o using dense optical flow to recover the structure -- I wouldn't have thought that would converge.
Love this a whole heck of a lot more than NeRF, or any other "lol lets just throw a huge network at it" approach.
Well yes, but that's what gaussian splatting also was. The question is: are their claims to be so much better than gsplat accurate?
There's no neural net with gaussian splatting, it's a fancy pointcloud that's optimized with ML techniques.
I know, that's the point.
Mea culpa, I misunderstood.