  Shape completion, i.e., predicting the complete geometry of an object from a
partial observation, is highly relevant for several downstream tasks, most
notably robotic manipulation. When basing planning or prediction of real grasps
on object shape reconstruction, an indication of severe geometric uncertainty
is indispensable. In particular, there can be an irreducible uncertainty in
extended regions about the presence of entire object parts when given ambiguous
object views. To treat this important case, we propose two novel methods for
predicting such uncertain regions as straightforward extensions of any method
for predicting local spatial occupancy, one through postprocessing occupancy
scores, the other through direct prediction of an uncertainty indicator. We
compare these methods together with two known approaches to probabilistic shape
completion. Moreover, we generate a dataset, derived from ShapeNet, of
realistically rendered depth images of object views with ground-truth
annotations for the uncertain regions. We train on this dataset and test each
method in shape completion and prediction of uncertain regions for known and
novel object instances and on synthetic and real data. While direct uncertainty
prediction is by far the most accurate in the segmentation of uncertain
regions, both novel methods outperform the two baselines in shape completion
and uncertain region prediction, and avoiding the predicted uncertain regions
increases the quality of grasps for all tested methods. Web:
https://github.com/DLR-RM/shape-completion
