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The neural networks representing these scenes take up just 5 MB... Less than the input images used to train them. Wow. Mind blowing!
Earlier quoted context omitted.
its a very similar concept to photogrammetry which is recovering a 3d representation of an object given pictures taken from different angles. In this work they take pictures of a scene from different angles and are able to train a neural network to render the scene from new angles that aren't in any source pictures. The neural network takes in a location (x,y,z), a viewing direction and spits out the RGB of the rende…
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 rep…
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.
Could someone ELI5, please?