[wokwokwok]

Significantly, the input is a sparse dataset.

ie. Few source images vs. traditional photogrammetry.

...but basically yes, tldr; photogrammetry using neural networks; this one is better than other recent attempts at the same thing, but takes a really long time (2 days for this vs 10 minutes for a voxel based approach in one of their comparisons).

Why bother?

mmm... theres some kind speculation you might be able to represent a photorealistic scene/ 3d object as a neural model instead of voxels or meshes.

That might be useful for some things. eg. say, a voxel representation of semi transparent fog, or high detail objects like hair are impractically huge, and as a mesh its very difficult to represent.

[rebuilder]

A number of things this seems to do well would be pretty much impossible with standard photogrammetry : trees with leaves, fine details like rigging on a ship, reflective surfaces, even refraction (!)

Of course the output is a new view, not a shaded mesh, but given it appears to generate depth data, I think you should be able to generate a point cloud and mesh it. Getting the materials from the output light even be possible, I'm not very up to date on the state of material capture nowadays.

[BubRoss]

> Significantly, the input is a sparse dataset. ie. Few source images vs. traditional photogrammetry.

This uses dozens or hundreds of images, which isn't usually necessary for traditional photogrammetry that maps photos to hard surfaces with textures.

I think what you noted about volumes is the significant part. Complex objects with fine detail and view dependent reflections are the part that shines here over photogrammetry, but it does take a lot of images. I didn't see anything in the paper that dealt with transparency.

[visarga]

> Why bother?

There might be 10x speedups to be gained with a tweaked model.