  We present a system for multi-level scene awareness for robotic manipulation.
Given a sequence of camera-in-hand RGB images, the system calculates three
types of information: 1) a point cloud representation of all the surfaces in
the scene, for the purpose of obstacle avoidance; 2) the rough pose of unknown
objects from categories corresponding to primitive shapes (e.g., cuboids and
cylinders); and 3) full 6-DoF pose of known objects. By developing and fusing
recent techniques in these domains, we provide a rich scene representation for
robot awareness. We demonstrate the importance of each of these modules, their
complementary nature, and the potential benefits of the system in the context
of robotic manipulation.
