In this earlier posting, I described a form of “photogrammetry” in which an arbitrary, coarse base-geometry is assumed as a starting point, and from which micropolygons are spawned, in order to approximate a more-detailed final geometry.

I must acknowledge that within this field, a domain also exists, which is *not* like that, and in which the computer tries to guess at a random, arbitrary geometry. Of course, this is a much more difficult form of the subject, and I do not know much about how it is intended to work.

I do know that aside from the fact that swatches of pixels need to be matched from one 2D photo to the next, one challenge which impedes this, is the fact that parts of the (yet-unknown) mesh will occlude each other to some camera-positions but not others, in ways that computers are poor at predicting. To deal with that requires such complex fields as “Constraint Satisfaction Programming” – aka ‘Logic Programming’, etc..

(Edit 01/05/2017 : Also, if we can assume that a 2D grid of pixel-swatches is being tagged for exact matching, and that only horizontal parallax is to be measured, the problem of entire rows of rectangles that all have the same signature can be cumbersome to code for, where only the end-points change position from one photo to the next… And then their signature can end, to be replaced by another, after which, on the same row, the first set of signatures can simply resume.

Further, If we knew that this approach was being used, Then we could safely infer that the number of mesh-units we derive, will also correspond to the number of rectangles, which each photo has been subdivided in to, not the number of pixels. )

If that was to succeed, I suppose it could again form a starting-point, for the micropolygon-based approach I was describing.

I do know of at least one consumer-grade product, which uses micropolygons.