Blind inverse light transport using unrolling network

Wenting Yin, Yuqi Li, Jiabao Li, Xulun Ye, Lijun Guo

Abstract: Passive Non-Line-Of-Sight (NLOS) imaging techniques have drawn increasing attention for their ability to recover hidden objects that are not visible in observed images. These methods only rely on indirect reflections from the scene surface without the need for expensive measurement equipment or complex measurement processes, in contrast to active NLOS imaging techniques. In this paper, we propose a novel unrolling network based on matrix factorization, which can jointly recover hidden video and light transport matrix. The network has multiple stages, each stage is equivalent to an optimized iteration of matrix factorization. We adopt spatial-variant convolution and neural representation of the light transport matrix in the network, resulting in higher efficiency. To evaluate the proposed method, we compare it with the state-of-the-art hidden video recovery methods on three public datasets. Both qualitative and quantitative comparisons show that our network outperforms the compared method in terms of robustness and accuracy.