  Computational approach to imaging around the corner, or non-line-of-sight
(NLOS) imaging, is becoming a reality thanks to major advances in imaging
hardware and reconstruction algorithms. A recent development towards practical
NLOS imaging, Nam et al. demonstrated a high-speed non-confocal imaging system
that operates at 5Hz, 100x faster than the prior art. This enormous gain in
acquisition rate, however, necessitates numerous approximations in light
transport, breaking many existing NLOS reconstruction methods that assume an
idealized image formation model. To bridge the gap, we present a novel deep
model that incorporates the complementary physics priors of wave propagation
and volume rendering into a neural network for high-quality and robust NLOS
reconstruction. This orchestrated design regularizes the solution space by
relaxing the image formation model, resulting in a deep model that generalizes
well on real captures despite being exclusively trained on synthetic data.
Further, we devise a unified learning framework that enables our model to be
flexibly trained using diverse supervision signals, including target intensity
images or even raw NLOS transient measurements. Once trained, our model renders
both intensity and depth images at inference time in a single forward pass,
capable of processing more than 5 captures per second on a high-end GPU.
Through extensive qualitative and quantitative experiments, we show that our
method outperforms prior physics and learning based approaches on both
synthetic and real measurements. We anticipate that our method along with the
fast capturing system will accelerate future development of NLOS imaging for
real world applications that require high-speed imaging.
