In this paper, we describe how scene depth can be extracted using a
hyperspectral light field capture (H-LF) system. Our H-LF system consists of a
5 x 6 array of cameras, with each camera sampling a different narrow band in
the visible spectrum. There are two parts to extracting scene depth. The first
part is our novel cross-spectral pairwise matching technique, which involves a
new spectral-invariant feature descriptor and its companion matching metric we
call bidirectional weighted normalized cross correlation (BWNCC). The second
part, namely, H-LF stereo matching, uses a combination of spectral-dependent
correspondence and defocus cues that rely on BWNCC. These two new cost terms
are integrated into a Markov Random Field (MRF) for disparity estimation.
Experiments on synthetic and real H-LF data show that our approach can produce
high-quality disparity maps. We also show that these results can be used to
produce the complete plenoptic cube in addition to synthesizing all-focus and
defocused color images under different sensor spectral responses.