  We describe a method to parse a complex, cluttered indoor scene into
primitives which offer a parsimonious abstraction of scene structure. Our
primitives are simple convexes. Our method uses a learned regression procedure
to parse a scene into a fixed number of convexes from RGBD input, and can
optionally accept segmentations to improve the decomposition. The result is
then polished with a descent method which adjusts the convexes to produce a
very good fit, and greedily removes superfluous primitives. Because the entire
scene is parsed, we can evaluate using traditional depth, normal, and
segmentation error metrics. Our evaluation procedure demonstrates that the
error from our primitive representation is comparable to that of predicting
depth from a single image.
