  While 3D shape representations enable powerful reasoning in many visual and
perception applications, learning 3D shape priors tends to be constrained to
the specific categories trained on, leading to an inefficient learning process,
particularly for general applications with unseen categories. Thus, we propose
PatchComplete, which learns effective shape priors based on multi-resolution
local patches, which are often more general than full shapes (e.g., chairs and
tables often both share legs) and thus enable geometric reasoning about unseen
class categories. To learn these shared substructures, we learn
multi-resolution patch priors across all train categories, which are then
associated to input partial shape observations by attention across the patch
priors, and finally decoded into a complete shape reconstruction. Such
patch-based priors avoid overfitting to specific train categories and enable
reconstruction on entirely unseen categories at test time. We demonstrate the
effectiveness of our approach on synthetic ShapeNet data as well as challenging
real-scanned objects from ScanNet, which include noise and clutter, improving
over state of the art in novel-category shape completion by 19.3% in chamfer
distance on ShapeNet, and 9.0% for ScanNet.
