  An unsupervised shape analysis is proposed to learn concepts reflecting shape
commonalities. Our approach is two-fold: i) a spatial topology analysis of
point cloud segment constellations within objects is used in which
constellations are decomposed and described in a hierarchical and symbolic
manner. ii) A topology analysis of the description space is used in which
segment decompositions are exposed in. Inspired by Persistent Homology, groups
of shape commonality are revealed. Experiments show that extracted persistent
commonality groups can feature semantically meaningful shape concepts; the
generalization of the proposed approach is evaluated by different real-world
datasets. We extend this by not only learning shape concepts using real-world
data, but by also using mental simulation of artificial abstract objects for
training purposes. This extended approach is unsupervised in two respects:
label-agnostic (no label information is used) and instance-agnostic (no
instances preselected by human supervision are used for training). Experiments
show that concepts generated with mental simulation, generalize and
discriminate real object observations. Consequently, a robot may train and
learn its own internal representation of concepts regarding shape appearance in
a self-driven and machine-centric manner while omitting the tedious process of
supervised dataset generation including the ambiguity in instance labeling and
selection.

Computer Vision and Pattern Recognition;Robotics