  This paper presents a novel optimization-based method for non-line-of-sight
(NLOS) imaging that aims to reconstruct hidden scenes under general setups with
significantly reduced reconstruction time. In NLOS imaging, the visible
surfaces of the target objects are notably sparse. To mitigate unnecessary
computations arising from empty regions, we design our method to render the
transients through partial propagations from a continuously sampled set of
points from the hidden space. Our method is capable of accurately and
efficiently modeling the view-dependent reflectance using surface normals,
which enables us to obtain surface geometry as well as albedo. In this
pipeline, we propose a novel domain reduction strategy to eliminate superfluous
computations in empty regions. During the optimization process, our domain
reduction procedure periodically prunes the empty regions from our sampling
domain in a coarse-to-fine manner, leading to substantial improvement in
efficiency. We demonstrate the effectiveness of our method in various NLOS
scenarios with sparse scanning patterns. Experiments conducted on both
synthetic and real-world data support the efficacy in general NLOS scenarios,
and the improved efficiency of our method compared to the previous
optimization-based solutions. Our code is available at
https://github.com/hyunbo9/domain-reduction-strategy.
