  3D object recognition has seen significant advances in recent years, showing
impressive performance on real-world 3D scan benchmarks, but lacking in object
part reasoning, which is fundamental to higher-level scene understanding such
as inter-object similarities or object functionality. Thus, we propose to
leverage large-scale synthetic datasets of 3D shapes annotated with part
information to learn Neural Part Priors (NPPs), optimizable spaces
characterizing geometric part priors. Crucially, we can optimize over the
learned part priors in order to fit to real-world scanned 3D scenes at test
time, enabling robust part decomposition of the real objects in these scenes
that also estimates the complete geometry of the object while fitting
accurately to the observed real geometry. Moreover, this enables global
optimization over geometrically similar detected objects in a scene, which
often share strong geometric commonalities, enabling scene-consistent part
decompositions. Experiments on the ScanNet dataset demonstrate that NPPs
significantly outperforms state of the art in part decomposition and object
completion in real-world scenes.
