  We present a novel semantic light field (LF) refocusing technique that can
achieve unprecedented see-through quality. Different from prior art, our
semantic see-through (SST) differentiates rays in their semantic meaning and
depth. Specifically, we combine deep learning and stereo matching to provide
each ray a semantic label. We then design tailored weighting schemes for
blending the rays. Although simple, our solution can effectively remove
foreground residues when focusing on the background. At the same time, SST
maintains smooth transitions in varying focal depths. Comprehensive experiments
on synthetic and new real indoor and outdoor datasets demonstrate the
effectiveness and usefulness of our technique.
