  Recent advances in 3D semantic scene understanding have shown impressive
progress in 3D instance segmentation, enabling object-level reasoning about 3D
scenes; however, a finer-grained understanding is required to enable
interactions with objects and their functional understanding. Thus, we propose
the task of part-based scene understanding of real-world 3D environments: from
an RGB-D scan of a scene, we detect objects, and for each object predict its
decomposition into geometric part masks, which composed together form the
complete geometry of the observed object. We leverage an intermediary part
graph representation to enable robust completion as well as building of part
priors, which we use to construct the final part mask predictions. Our
experiments demonstrate that guiding part understanding through part graph to
part prior-based predictions significantly outperforms alternative approaches
to the task of semantic part completion.
