Non-line-of-sight imaging employs ultra-fast illumination and sensing devices to reconstruct scenes outside their line of sight by analyzing the temporal profile of indirect scattered illumination on a secondary relay surface. Commonly, the NLOS methods transform the temporal domain into the frequency domain and operate on it, and then identify surface locations by locating the maxima in amplitude along the reconstruction volume. Phase information, which is virtual as it results from a Fourier transform, is very often discarded or ignored. We incorporate phase information into our novel Zero-Phase Phasor Fields imaging technique, which we derive for a confocal capture configuration. We show how, at positions that belong to the hidden geometry, we can ensure the phase is zero, so we can locate the hidden geometry with great precision by locating the zero crossings in the phase. This allows us to reconstruct at widely spaced locations and still achieve up to 125 micrometer depth precision, as our experimental validation shows with both synthetic and captured data, the latter publicly available. Moreover, the phase is robust to noise, as we demonstrate with decreasing signal-to-noise ratio using publicly available dataset captures of the same scene.

Non-line-of-sight imaging methods reconstruct images from light captured off a relay surface. In most prior work this relay surface is a diffuse plane. It has been shown that even small deviations from a planar relay wall geometry quickly degrade reconstruction quality. Although existing methods can account for relay surface geometry in a straightforward way, they typically have high computational complexity and take orders of magnitude longer time to compute than state-of-the-art planar methods. In this work, we propose a fast algorithm that can perform non-line-of-sight reconstruction on arbitrary non-planar relay surfaces. Our algorithm has the same computational and memory complexity as the fastest existing algorithms, yet it achieves comparable reconstruction quality to the widely-used slower algorithms.

Non-line-of-sight (NLOS) imaging methods are capable of reconstructing complex scenes that are not visible to an observer using indirect illumination. However, they assume only third-bounce illumination, so they are currently limited to single-corner configurations, and present limited visibility when imaging surfaces at certain orientations. To reason about and tackle these limitations, we make the key observation that planar diffuse surfaces behave specularly at wavelengths used in the computational wave-based NLOS imaging domain. We call such surfaces virtual mirrors. We leverage this observation to expand the capabilities of NLOS imaging using illumination beyond the third bounce, addressing two problems: imaging single-corner objects at limited visibility angles, and imaging objects hidden behind two corners. To image objects at limited visibility angles, we first analyze the reflections of the known illuminated point on surfaces of the scene as an estimator of the position and orientation of objects with limited visibility. We then image those limited visibility objects by computationally building secondary apertures at other surfaces that observe the target object from a direct visibility perspective. Beyond single-corner NLOS imaging, we exploit the specular behavior of virtual mirrors to image objects hidden behind a second corner by imaging the space behind such virtual mirrors, where the mirror image of objects hidden around two corners is formed. No specular surfaces were involved in the making of this paper.

Recent advances in computer vision and inverse light transport theory have resulted in several non-line-of-sight imaging techniques. These techniques use photon time-of-flight information encoded in light after multiple, diffuse reflections to reconstruct a three-dimensional scene. In this paper, we propose and describe two iterative backprojection algorithms, the additive error backprojection (AEB) and multiplicative error backprojection (MEB), whose goal is to improve the reconstruction of the scene under investigation over non-iterative backprojection algorithms. We evaluate the proposed algorithms' performance applied to simulated and real data (gathered from an experimental setup where the system needs to reconstruct an unknown scene). Results show that the proposed iterative algorithms are able to provide better reconstruction than the unfiltered, non-iterative backprojection algorithm for both simulated and physical scenes, but are more sensitive to errors in the light transport model.