  Current end-to-end grasp planning methods propose grasps in the order of
seconds that attain high grasp success rates on a diverse set of objects, but
often by constraining the workspace to top-grasps. In this work, we present a
method that allows end-to-end top-grasp planning methods to generate full
six-degree-of-freedom grasps using a single RGB-D view as input. This is
achieved by estimating the complete shape of the object to be grasped, then
simulating different viewpoints of the object, passing the simulated viewpoints
to an end-to-end grasp generation method, and finally executing the overall
best grasp. The method was experimentally validated on a Franka Emika Panda by
comparing 429 grasps generated by the state-of-the-art Fully Convolutional
Grasp Quality CNN, both on simulated and real camera images. The results show
statistically significant improvements in terms of grasp success rate when
using simulated images over real camera images, especially when the real camera
viewpoint is angled. Code and video are available at
https://irobotics.aalto.fi/beyond-top-grasps-through-scene-completion/.
