  Being able to grasp objects is a fundamental component of most robotic
manipulation systems. In this paper, we present a new approach to
simultaneously reconstruct a mesh and a dense grasp quality map of an object
from a depth image. At the core of our approach is a novel camera-centric
object representation called the "object shell" which is composed of an
observed "entry image" and a predicted "exit image". We present an
image-to-image residual ConvNet architecture in which the object shell and a
grasp-quality map are predicted as separate output channels. The main advantage
of the shell representation and the corresponding neural network architecture,
ShellGrasp-Net, is that the input-output pixel correspondences in the shell
representation are explicitly represented in the architecture. We show that
this coupling yields superior generalization capabilities for object
reconstruction and accurate grasp quality estimation implicitly considering the
object geometry. Our approach yields an efficient dense grasp quality map and
an object geometry estimate in a single forward pass. Both of these outputs can
be used in a wide range of robotic manipulation applications. With rigorous
experimental validation, both in simulation and on a real setup, we show that
our shell-based method can be used to generate precise grasps and the
associated grasp quality with over 90% accuracy. Diverse grasps computed on
shell reconstructions allow the robot to select and execute grasps in cluttered
scenes with more than 93% success rate.
