  We propose the Part-based Recurrent Multi-view Aggregation network(PREMA) to
eliminate the detrimental effects of the practical view defects, such as
insufficient view numbers, occlusions or background clutters, and also enhance
the discriminative ability of shape representations. Inspired by the fact that
human recognize an object mainly by its discriminant parts, we define the
multi-view coherent part(MCP), a discriminant part reoccurring in different
views. Our PREMA can reliably locate and effectively utilize MCPs to build
robust shape representations. Comprehensively, we design a novel Regional
Attention Unit(RAU) in PREMA to compute the confidence map for each view, and
extract MCPs by applying those maps to view features. PREMA accentuates MCPs
via correlating features of different views, and aggregates the part-aware
features for shape representation.
