  Various 3D semantic attributes such as segmentation masks, geometric
features, keypoints, and materials can be encoded as per-point probe functions
on 3D geometries. Given a collection of related 3D shapes, we consider how to
jointly analyze such probe functions over different shapes, and how to discover
common latent structures using a neural network --- even in the absence of any
correspondence information. Our network is trained on point cloud
representations of shape geometry and associated semantic functions on that
point cloud. These functions express a shared semantic understanding of the
shapes but are not coordinated in any way. For example, in a segmentation task,
the functions can be indicator functions of arbitrary sets of shape parts, with
the particular combination involved not known to the network. Our network is
able to produce a small dictionary of basis functions for each shape, a
dictionary whose span includes the semantic functions provided for that shape.
Even though our shapes have independent discretizations and no functional
correspondences are provided, the network is able to generate latent bases, in
a consistent order, that reflect the shared semantic structure among the
shapes. We demonstrate the effectiveness of our technique in various
segmentation and keypoint selection applications.
