  We consider the problem of learning object arrangements in a 3D scene. The
key idea here is to learn how objects relate to human poses based on their
affordances, ease of use and reachability. In contrast to modeling
object-object relationships, modeling human-object relationships scales
linearly in the number of objects. We design appropriate density functions
based on 3D spatial features to capture this. We learn the distribution of
human poses in a scene using a variant of the Dirichlet process mixture model
that allows sharing of the density function parameters across the same object
types. Then we can reason about arrangements of the objects in the room based
on these meaningful human poses. In our extensive experiments on 20 different
rooms with a total of 47 objects, our algorithm predicted correct placements
with an average error of 1.6 meters from ground truth. In arranging five real
scenes, it received a score of 4.3/5 compared to 3.7 for the best baseline
method.
