  Given a point cloud, we consider inferring kinematic models of 3D articulated
objects such as boxes for the purpose of manipulating them. While previous work
has shown how to extract a planar kinematic model (often represented as a
linear chain), such planar models do not apply to 3D objects that are composed
of segments often linked to the other segments in cyclic configurations. We
present an approach for building a model that captures the relation between the
input point cloud features and the object segment as well as the relation
between the neighboring object segments. We use a conditional random field that
allows us to model the dependencies between different segments of the object.
We test our approach on inferring the kinematic structure from partial and
noisy point cloud data for a wide variety of boxes including cake boxes, pizza
boxes, and cardboard cartons of several sizes. The inferred structure enables
our robot to successfully close these boxes by manipulating the flaps.
