Multi-modal Non-line-of-sight Passive Imaging

TL;DR

This paper introduces a passive, multi-modal imaging method for non-line-of-sight scenarios using the spatial coherence of reflected light, enabling scene reconstruction without active illumination or traditional imaging optics.

Contribution

It develops a convex optimization framework that fuses intensity and coherence data for NLOS imaging, advancing passive scene reconstruction techniques.

Findings

Effective reconstruction of obscured objects using coherence data

Robustness to partially coherent fields in indoor and outdoor environments

Efficient algorithm for solving the convex optimization problem

Abstract

We consider the non-line-of-sight (NLOS) imaging of an object using the light reflected off a diffusive wall. The wall scatters incident light such that a lens is no longer useful to form an image. Instead, we exploit the 4D spatial coherence function to reconstruct a 2D projection of the obscured object. The approach is completely passive in the sense that no control over the light illuminating the object is assumed and is compatible with the partially coherent fields ubiquitous in both the indoor and outdoor environments. We formulate a multi-criteria convex optimization problem for reconstruction, which fuses the reflected field's intensity and spatial coherence information at different scales. Our formulation leverages established optics models of light propagation and scattering and exploits the sparsity common to many images in different bases. We also develop an algorithm based on the alternating direction method of multipliers to efficiently solve the convex program proposed. A means for analyzing the null space of the measurement matrices is provided as well as a means for weighting the contribution of individual measurements to the reconstruction. This paper holds promise to advance passive imaging in the challenging NLOS regimes in which the intensity does not necessarily retain distinguishable features and provides a framework for multi-modal information fusion for efficient scene reconstruction.