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Passive None-line-of-sight imaging with arbitrary scene condition and detection pattern in small amount of prior data by Gui, Yunting; Fu, Yuegang; Xiao, Xueming; Yao, Meibao is a scholarly article available to read on EtoBox.

What is Passive None-line-of-sight imaging with arbitrary scene condition and detection pattern in small amount of prior data about?

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

Author

Gui, Yunting; Fu, Yuegang; Xiao, Xueming; Yao, Meibao

Published

2024

Language

EN