Domain Reduction Strategy for Non Line of Sight Imaging
Hyunbo Shim In Cho Daekyu Kwon Seon Joo Kim
This paper presents a novel optimization-based method for non-line-of-sight
(NLOS) imaging that aims to reconstruct hidden scenes under various setups. Our
method is built upon the observation that photons returning from each point in
hidden volumes can be independently computed if the interactions between hidden
surfaces are trivially ignored. We model the generalized light propagation
function to accurately represent the transients as a linear combination of
these functions. Moreover, our proposed method includes a domain reduction
procedure to exclude empty areas of the hidden volumes from the set of
propagation functions, thereby improving computational efficiency of the
optimization. We demonstrate the effectiveness of the method in various NLOS
scenarios, including non-planar relay wall, sparse scanning patterns, confocal
and non-confocal, and surface geometry reconstruction. Experiments conducted on
both synthetic and real-world data clearly support the superiority and the
efficiency of the proposed method in general NLOS scenarios.