  It has been a longstanding goal in computer vision to describe the 3D
physical space in terms of parameterized volumetric models that would allow
autonomous machines to understand and interact with their surroundings. Such
models are typically motivated by human visual perception and aim to represents
all elements of the physical word ranging from individual objects to complex
scenes using a small set of parameters. One of the de facto stadards to
approach this problem are superquadrics - volumetric models that define various
3D shape primitives and can be fitted to actual 3D data (either in the form of
point clouds or range images). However, existing solutions to superquadric
recovery involve costly iterative fitting procedures, which limit the
applicability of such techniques in practice. To alleviate this problem, we
explore in this paper the possibility to recover superquadrics from range
images without time consuming iterative parameter estimation techniques by
using contemporary deep-learning models, more specifically, convolutional
neural networks (CNNs). We pose the superquadric recovery problem as a
regression task and develop a CNN regressor that is able to estimate the
parameters of a superquadric model from a given range image. We train the
regressor on a large set of synthetic range images, each containing a single
(unrotated) superquadric shape and evaluate the learned model in comparaitve
experiments with the current state-of-the-art. Additionally, we also present a
qualitative analysis involving a dataset of real-world objects. The results of
our experiments show that the proposed regressor not only outperforms the
existing state-of-the-art, but also ensures a 270x faster execution time.
