Domain Reduction Strategy for Non Line of Sight Imaging
This addresses the challenge of slow reconstruction in NLOS imaging for applications like surveillance or robotics, though it appears incremental as it builds on existing optimization-based methods.
The paper tackles the problem of non-line-of-sight (NLOS) imaging by proposing an optimization-based method that reconstructs hidden scenes with significantly reduced reconstruction time, achieving substantial efficiency improvements compared to previous solutions.
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.