  Generative models for 3D shapes represented by hierarchies of parts can
generate realistic and diverse sets of outputs. However, existing models suffer
from the key practical limitation of modelling shapes holistically and thus
cannot perform conditional sampling, i.e. they are not able to generate
variants on individual parts of generated shapes without modifying the rest of
the shape. This is limiting for applications such as 3D CAD design that involve
adjusting created shapes at multiple levels of detail. To address this, we
introduce LSD-StructureNet, an augmentation to the StructureNet architecture
that enables re-generation of parts situated at arbitrary positions in the
hierarchies of its outputs. We achieve this by learning individual,
probabilistic conditional decoders for each hierarchy depth. We evaluate
LSD-StructureNet on the PartNet dataset, the largest dataset of 3D shapes
represented by hierarchies of parts. Our results show that contrarily to
existing methods, LSD-StructureNet can perform conditional sampling without
impacting inference speed or the realism and diversity of its outputs.
