  Existing light field representations, such as epipolar plane image (EPI) and
sub-aperture images, do not consider the structural characteristics across the
views, so they usually require additional disparity and spatial structure cues
for follow-up tasks. Besides, they have difficulties dealing with occlusions or
larger disparity scenes. To this end, this paper proposes a novel Epipolar
Focus Spectrum (EFS) representation by rearranging the EPI spectrum. Different
from the classical EPI representation where an EPI line corresponds to a
specific depth, there is a one-to-one mapping from the EFS line to the view.
Accordingly, compared to a sparsely-sampled light field, a densely-sampled one
with the same field of view (FoV) leads to a more compact distribution of such
linear structures in the double-cone-shaped region with the identical opening
angle in its corresponding EFS. Hence the EFS representation is invariant to
the scene depth. To demonstrate its effectiveness, we develop a trainable
EFS-based pipeline for light field reconstruction, where a dense light field
can be reconstructed by compensating the "missing EFS lines" given a sparse
light field, yielding promising results with cross-view consistency, especially
in the presence of severe occlusion and large disparity. Experimental results
on both synthetic and real-world datasets demonstrate the validity and
superiority of the proposed method over SOTA methods.
