  We explore the interpretability of 3D geometric deep learning models in the
context of Computer-Aided Design (CAD). The field of parametric CAD can be
limited by the difficulty of expressing high-level design concepts in terms of
a few numeric parameters. In this paper, we use a deep learning architectures
to encode high dimensional 3D shapes into a vectorized latent representation
that can be used to describe arbitrary concepts. Specifically, we train a
simple auto-encoder to parameterize a dataset of complex shapes. To understand
the latent encoded space, we use the idea of Concept Activation Vectors (CAV)
to reinterpret the latent space in terms of user-defined concepts. This allows
modification of a reference design to exhibit more or fewer characteristics of
a chosen concept or group of concepts. We also test the statistical
significance of the identified concepts and determine the sensitivity of a
physical quantity of interest across the dataset.
