You said : "- detecting lines from pixels - detecting geometry in pointclouds - constructing 3D from stereo images, photogrammetry, 360 panoramas"

  ==> For me it is more something like :
   Source = crude video-or-photo pixels  (to) ===> Find simple many rectangle-surface  that are glued together one another.

I kind of did a version of what you suggest - I think I linked to a video showing plane edges auto-detected in a pointcloud sample.

Similarly I use another algo to detect pipe runs which tend to appear as half cylinders in the pointcloud, as the scanner usually sees one side, and often the other side is hidden, hard to access, up against a wall.

So, I guess my point is the devil is in the details .. and machine learning can optimize even further on good heuristics we might come up with.

Also, when you go thru a whole pointcloud, you have a lot of data to sift thru, so you want something fairly efficient, even if your using multiple GPUs do do the heavy matmull lifting.

You can think of RL as an optimization - greatly speeding up something like monte carlo tree search, by learning to guess the best solution earlier.