  Reliable object grasping is a crucial capability for autonomous robots.
However, many existing grasping approaches focus on general clutter removal
without explicitly modeling objects and thus only relying on the visible local
geometry. We introduce CenterGrasp, a novel framework that combines object
awareness and holistic grasping. CenterGrasp learns a general object prior by
encoding shapes and valid grasps in a continuous latent space. It consists of
an RGB-D image encoder that leverages recent advances to detect objects and
infer their pose and latent code, and a decoder to predict shape and grasps for
each object in the scene. We perform extensive experiments on simulated as well
as real-world cluttered scenes and demonstrate strong scene reconstruction and
6-DoF grasp-pose estimation performance. Compared to the state of the art,
CenterGrasp achieves an improvement of 38.5 mm in shape reconstruction and 33
percentage points on average in grasp success. We make the code and trained
models publicly available at http://centergrasp.cs.uni-freiburg.de.
