  Localizing stereo boundaries and predicting nearby disparities are difficult
because stereo boundaries induce occluded regions where matching cues are
absent. Most modern computer vision algorithms treat occlusions secondarily
(e.g., via left-right consistency checks after matching) or rely on high-level
cues to improve nearby disparities (e.g., via deep networks and large training
sets). They ignore the geometry of stereo occlusions, which dictates that the
spatial extent of occlusion must equal the amplitude of the disparity jump that
causes it. This paper introduces an energy and level-set optimizer that
improves boundaries by encoding occlusion geometry. Our model applies to
two-layer, figure-ground scenes, and it can be implemented cooperatively using
messages that pass predominantly between parents and children in an undecimated
hierarchy of multi-scale image patches. In a small collection of figure-ground
scenes curated from Middlebury and Falling Things stereo datasets, our model
provides more accurate boundaries than previous occlusion-handling stereo
techniques. This suggests new directions for creating cooperative stereo
systems that incorporate occlusion cues in a human-like manner.
