Abstract. 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.