  Methods that use neural networks for synthesizing 3D shapes in the form of a
part-based representation have been introduced over the last few years. These
methods represent shapes as a graph or hierarchy of parts and enable a variety
of applications such as shape sampling and reconstruction. However, current
methods do not allow easily regenerating individual shape parts according to
user preferences. In this paper, we investigate techniques that allow the user
to generate multiple, diverse suggestions for individual parts. Specifically,
we experiment with multimodal deep generative models that allow sampling
diverse suggestions for shape parts and focus on models which have not been
considered in previous work on shape synthesis. To provide a comparative study
of these techniques, we introduce a method for synthesizing 3D shapes in a
part-based representation and evaluate all the part suggestion techniques
within this synthesis method. In our method, which is inspired by previous
work, shapes are represented as a set of parts in the form of implicit
functions which are then positioned in space to form the final shape. Synthesis
in this representation is enabled by a neural network architecture based on an
implicit decoder and a spatial transformer. We compare the various multimodal
generative models by evaluating their performance in generating part
suggestions. Our contribution is to show with qualitative and quantitative
evaluations which of the new techniques for multimodal part generation perform
the best and that a synthesis method based on the top-performing techniques
allows the user to more finely control the parts that are generated in the 3D
shapes while maintaining high shape fidelity when reconstructing shapes.
