  In this paper, we present a novel dataset named MVB (Multi View Baggage) for
baggage ReID task which has some essential differences from person ReID. The
features of MVB are three-fold. First, MVB is the first publicly released
large-scale dataset that contains 4519 baggage identities and 22660 annotated
baggage images as well as its surface material labels. Second, all baggage
images are captured by specially-designed multi-view camera system to handle
pose variation and occlusion, in order to obtain the 3D information of baggage
surface as complete as possible. Third, MVB has remarkable inter-class
similarity and intra-class dissimilarity, considering the fact that baggage
might have very similar appearance while the data is collected in two real
airport environments, where imaging factors varies significantly from each
other. Moreover, we proposed a merged Siamese network as baseline model and
evaluated its performance. Experiments and case study are conducted on MVB.
