  Object shape is a key cue that contributes to the semantic understanding of
objects. In this work we focus on the categorization of real-world object point
clouds to particular shape types. Therein surface description and
representation of object shape structure have significant influence on shape
categorization accuracy, when dealing with real-world scenes featuring noisy,
partial and occluded object observations. An unsupervised hierarchical learning
procedure is utilized here to symbolically describe surface characteristics on
multiple semantic levels. Furthermore, a constellation model is proposed that
hierarchically decomposes objects. The decompositions are described as
constellations of symbols (shape motifs) in a gradual order, hence reflecting
shape structure from local to global, i.e., from parts over groups of parts to
entire objects. The combination of this multi-level description of surfaces and
the hierarchical decomposition of shapes leads to a representation which allows
to conceptualize shapes. An object discrimination has been observed in
experiments with seven categories featuring instances with sensor noise,
occlusions as well as inter-category and intra-category similarities.
Experiments include the evaluation of the proposed description and shape
decomposition approach, and comparisons to Fast Point Feature Histograms, a
Vocabulary Tree and a neural network-based Deep Learning method. Furthermore,
experiments are conducted with alternative datasets which analyze the
generalization capability of the proposed approach.
