Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge Occluder

TL;DR

This paper introduces a novel method for 2D non-line-of-sight scene reconstruction using a single edge occluder, leveraging a new forward model and inversion algorithms to extract spatial information from penumbra variations.

Contribution

It extends previous 1D NLOS imaging techniques by adding a second dimension—range—using a new model and algorithms for 2D scene reconstruction from a single photograph.

Findings

Successful 2D reconstructions demonstrated on experimental data.

New forward model accounts for radial falloff effects.

Cramer-Rao bound analysis confirms the method's feasibility.

Abstract

Passive non-line-of-sight imaging methods are often faster and stealthier than their active counterparts, requiring less complex and costly equipment. However, many of these methods exploit motion of an occluder or the hidden scene, or require knowledge or calibration of complicated occluders. The edge of a wall is a known and ubiquitous occluding structure that may be used as an aperture to image the region hidden behind it. Light from around the corner is cast onto the floor forming a fan-like penumbra rather than a sharp shadow. Subtle variations in the penumbra contain a remarkable amount of information about the hidden scene. Previous work has leveraged the vertical nature of the edge to demonstrate 1D (in angle measured around the corner) reconstructions of moving and stationary hidden scenery from as little as a single photograph of the penumbra. In this work, we introduce a second reconstruction dimension: range measured from the edge. We derive a new forward model, accounting for radial falloff, and propose two inversion algorithms to form 2D reconstructions from a single photograph of the penumbra. Performances of both algorithms are demonstrated on experimental data corresponding to several different hidden scene configurations. A Cramer-Rao bound analysis further demonstrates the feasibility (and utility) of the 2D corner camera.