  In this paper we address the problem of representing 3D visual data with
parameterized volumetric shape primitives. Specifically, we present a
(two-stage) approach built around convolutional neural networks (CNNs) capable
of segmenting complex depth scenes into the simpler geometric structures that
can be represented with superquadric models. In the first stage, our approach
uses a Mask RCNN model to identify superquadric-like structures in depth scenes
and then fits superquadric models to the segmented structures using a specially
designed CNN regressor. Using our approach we are able to describe complex
structures with a small number of interpretable parameters. We evaluated the
proposed approach on synthetic as well as real-world depth data and show that
our solution does not only result in competitive performance in comparison to
the state-of-the-art, but is able to decompose scenes into a number of
superquadric models at a fraction of the time required by competing approaches.
We make all data and models used in the paper available from
https://lmi.fe.uni-lj.si/en/research/resources/sq-seg.
