  We propose PSSNet, a network architecture for generating diverse plausible 3D
reconstructions from a single 2.5D depth image. Existing methods tend to
produce only small variations on a single shape, even when multiple shapes are
consistent with an observation. To obtain diversity we alter a Variational Auto
Encoder by providing a learned shape bounding box feature as side information
during training. Since these features are known during training, we are able to
add a supervised loss to the encoder and noiseless values to the decoder. To
evaluate, we sample a set of completions from a network, construct a set of
plausible shape matches for each test observation, and compare using our
plausible diversity metric defined over sets of shapes. We perform experiments
using Shapenet mugs and partially-occluded YCB objects and find that our method
performs comparably in datasets with little ambiguity, and outperforms existing
methods when many shapes plausibly fit an observed depth image. We demonstrate
one use for PSSNet on a physical robot when grasping objects in occlusion and
clutter.
