Compressive sparse and low-rank recovery (CSLR) is a novel method
for compressed sensing deriving a low-rank and a sparse data terms
from randomized projection measurements. While previous approaches
either applied compressive measurements to phenomena assumed to be
sparse or explicitly assume and measure low-rank approximations,
CSLR is inherently robust if any such assumption might be
violated. In this paper, we will derive CSLR using Fixed-Point
Continuation algorithms, and extend this approach in order to
exploit the correlation in high-order dimensions to further reduce
the number of captured samples. Though generally applicable, we
demonstrate the effectiveness of our approach on data sets captured
with a novel hyperspectral light stage that can emit a distinct
spectrum from each of the 196 light source directions enabling
bispectral measurements of reflectance from arbitrary
viewpoints. Bispectral reflectance fields and BTFs are faithfully
reconstructed from a small number of compressed measurements
