  Establishing dense correspondences between multiple images is a fundamental
task in many applications. However, finding a reliable correspondence in
multi-modal or multi-spectral images still remains unsolved due to their
challenging photometric and geometric variations. In this paper, we propose a
novel dense descriptor, called dense adaptive self-correlation (DASC), to
estimate multi-modal and multi-spectral dense correspondences. Based on an
observation that self-similarity existing within images is robust to imaging
modality variations, we define the descriptor with a series of an adaptive
self-correlation similarity measure between patches sampled by a randomized
receptive field pooling, in which a sampling pattern is obtained using a
discriminative learning. The computational redundancy of dense descriptors is
dramatically reduced by applying fast edge-aware filtering. Furthermore, in
order to address geometric variations including scale and rotation, we propose
a geometry-invariant DASC (GI-DASC) descriptor that effectively leverages the
DASC through a superpixel-based representation. For a quantitative evaluation
of the GI-DASC, we build a novel multi-modal benchmark as varying photometric
and geometric conditions. Experimental results demonstrate the outstanding
performance of the DASC and GI-DASC in many cases of multi-modal and
multi-spectral dense correspondences.
