  This paper addresses the problem of reassembling images from disjointed
fragments. More specifically, given an unordered set of fragments, we aim at
reassembling one or several possibly incomplete images. The main contributions
of this work are: 1) several deep neural architectures to predict the relative
position of image fragments that outperform the previous state of the art; 2)
casting the reassembly problem into the shortest path in a graph problem for
which we provide several construction algorithms depending on available
information; 3) a new dataset of images taken from the Metropolitan Museum of
Art (MET) dedicated to image reassembly for which we provide a clear setup and
a strong baseline.
