  This paper presents Slime, a novel non-deep image matching framework which
models the scene as rough local overlapping planes. This intermediate
representation sits in-between the local affine approximation of the keypoint
patches and the global matching based on both spatial and similarity
constraints, providing a progressive pruning of the correspondences, as planes
are easier to handle with respect to general scenes.
  Slime decomposes the images into overlapping regions at different scales and
computes loose planar homographies. Planes are mutually extended by compatible
matches and the images are split into fixed tiles, with only the best
homographies retained for each pair of tiles. Stable matches are identified
according to the consensus of the admissible stereo configurations provided by
pairwise homographies. Within tiles, the rough planes are then merged according
to their overlap in terms of matches and further consistent correspondences are
extracted.
  The whole process only involves homography constraints. As a result, both the
coverage and the stability of correct matches over the scene are amplified,
together with the ability to spot matches in challenging scenes, allowing
traditional hybrid matching pipelines to make up lost ground against recent
end-to-end deep matching methods.
  In addition, the paper gives a thorough comparative analysis of recent
state-of-the-art in image matching represented by end-to-end deep networks and
hybrid pipelines. The evaluation considers both planar and non-planar scenes,
taking into account critical and challenging scenarios including abrupt
temporal image changes and strong variations in relative image rotations.
According to this analysis, although the impressive progress done in this
field, there is still a wide room for improvements to be investigated in future
research.
