  Dense packing in pick-and-place systems is an important feature in many
warehouse and logistics applications. Prior work in this space has largely
focused on planning algorithms in simulation, but real-world packing
performance is often bottlenecked by the difficulty of perceiving 3D object
geometry in highly occluded, partially observed scenes. In this work, we
present a fully-convolutional shape completion model, F-CON, which can be
easily combined with off-the-shelf planning methods for dense packing in the
real world. We also release a simulated dataset, COB-3D-v2, that can be used to
train shape completion models for real-word robotics applications, and use it
to demonstrate that F-CON outperforms other state-of-the-art shape completion
methods. Finally, we equip a real-world pick-and-place system with F-CON, and
demonstrate dense packing of complex, unseen objects in cluttered scenes.
Across multiple planning methods, F-CON enables substantially better dense
packing than other shape completion methods.
