  Passive Non-Line-of-Sight (NLOS) imaging requires to reconstruct objects
which cannot be seen in line without using external controllable light sources.
It can be widely applied in areas like counter-terrorism, urban-Warfare,
autonomous-driving and robot-vision. Existing methods for passive NLOS
typically required extensive prior information and significant computational
resources to establish light transport matrices or train neural networks. These
constraints pose significant challenges for transitioning models to different
NLOS scenarios. Thus, the pressing issue in passive NLOS imaging currently lies
in whether it is possible to estimate the light transport matrices which
corresponding to relay surfaces and scenes, as well as the specific
distribution of targets, with a small amount of prior knowledge. In this work,
we hypothesized a high-dimensional manifold and mathematically proved its
existence. Within this high-dimensional manifold, the structural information of
obscured targets is minimally disrupted. Therefore, we proposed a universal
framework named High-Dimensional Projection Selection (HDPS) which can
establish this high-dimensional manifold and output its projection onto
corresponding surfaces on low-dimensional. HDPS can be applied to most mature
network architectures and estimate the distribution of target and light spot
obtained by camera with only minimal prior data. Certainly, with the help of
the estimated information, it can establish a high-dimensional manifold
consisting of target and input. As demonstrated in experiment, our framework,
even when applied to the most basic network structures, can achieve higher
accuracy results with significantly smaller amounts of prior data. Thereby, our
approach enables passive NLOS scenarios to reconstruct target by limited prior
data and computational resources.
