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In a built environment, wanting to see without direct line of sight is often due to being outside a doorway. The two vertical edges of the doorway provide occlusions that can be exploited for non-line-of-sight imaging by forming corner cameras. While each corner camera can separately yield a robust 1D reconstruction, joint processing suggests novelties in both forward modeling and inversion. The resulting doorway camera provides accurate and robust 2D reconstructions of the hidden scene. This work provides a novel inversion algorithm to jointly estimate two views of change in the hidden scene, using the temporal difference between photographs acquired on the visible side of the doorway. Successful reconstruction is demonstrated in a variety of real and rendered scenarios, including different hidden scenes and lighting conditions. A Cramer-Rao bound analysis is used to demonstrate the 2D resolving power of the doorway camera over other passive acquisition strategies and to motivate the novel biangular reconstruction grid. 

Abstract The measurement of the optical transmission matrix (TM) of an opaque material is an advanced form of space-variant aberration correction. Beyond imaging, TM-based methods are emerging in a range of fields, including optical communications, micro-manipulation, and computing. In many cases, the TM is very sensitive to perturbations in the configuration of the scattering medium it represents. Therefore, applications often require an up-to-the-minute characterisation of the fragile TM, typically entailing hundreds to thousands of probe measurements. Here, we explore how these measurement requirements can be relaxed using the framework of compressive sensing, in which the incorporation of prior information enables accurate estimation from fewer measurements than the dimensionality of the TM we aim to reconstruct. Examples of such priors include knowledge of a memory effect linking the input and output fields, an approximate model of the optical system, or a recent but degraded TM measurement. We demonstrate this concept by reconstructing the full-size TM of a multimode fibre supporting 754 modes at compression ratios down to ∼5% with good fidelity. We show that in this case, imaging is still possible using TMs reconstructed at compression ratios down to ∼1% (eight probe measurements). This compressive TM sampling strategy is quite general and may be applied to a variety of other scattering samples, including diffusers, thin layers of tissue, fibre optics of any refractive profile, and reflections from opaque walls. These approaches offer a route towards the measurement of high-dimensional TMs either quickly or with access to limited numbers of measurements. 

Edge-resolved transient imaging (ERTI) is a method for non-line-of-sight imaging that combines the use of direct time of flight for measuring distances with the azimuthal angular resolution afforded by a vertical edge occluder. Recently conceived and demonstrated for the first time, no performance analyses or optimizations of ERTI have appeared in published papers. This paper explains how the difficulty of detection of hidden scene objects with ERTI depends on a variety of parameters, including illumination power, acquisition time, ambient light, visible-side reflectivity, hidden-side reflectivity, target range, and target azimuthal angular position. Based on this analysis, optimization of the acquisition process is introduced whereby the illumination dwell times are varied to counteract decreasing signal-to-noise ratio at deeper angles into the hidden volume. Inaccuracy caused by a coaxial approximation is also analyzed and simulated. 

Non-line-of-sight (NLOS) imaging is a rapidly growing field seeking to form images of objects outside the field of view, with potential applications in autonomous navigation, reconnaissance, and even medical imaging. The critical challenge of NLOS imaging is that diffuse reflections scatter light in all directions, resulting in weak signals and a loss of directional information. To address this problem, we propose a method for seeing around corners that derives angular resolution from vertical edges and longitudinal resolution from the temporal response to a pulsed light source. We introduce an acquisition strategy, scene response model, and reconstruction algorithm that enable the formation of 2.5-dimensional representations—a plan view plus heights—and a 180^∘field of view for large-scale scenes. Our experiments demonstrate accurate reconstructions of hidden rooms up to 3 meters in each dimension despite a small scan aperture (1.5-centimeter radius) and only 45 measurement locations.