Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications. With coherent illumination, the light scattered from diffusive walls forms speckle patterns that contain information of the hidden object. It is possible to realize non-line-of-sight (NLOS) recognition with these speckle patterns. We introduce a novel approach based on speckle pattern recognition with deep neural network, which is simpler and more robust than other NLOS recognition methods. Simulations and experiments are performed to verify the feasibility and performance of this approach.
NLOS imaging with TOF has recently received considerable attention. It is a range imaging system that resolves the distance based on measuring the TOF of a light signal between the object and the camera for each point of the image. The mechanism of TOF measurement without line-of-sight is as follows @cite_28 : A laser pulse hits a wall that scatters the light diffusely to a hidden object; then, the light returns to the wall and is captured by a camera. By changing the position of the laser beam on the wall with a set of galvanometer-actuated mirrors, the shape of the hidden object can be determined.
@cite_28
2755257996 2799016823 2299392318 2952225226
Non-line-of-sight (NLOS) imaging utilizes the full 5D light transient measurements to reconstruct scenes beyond the cameras field of view. Mathematically, this requires solving an elliptical tomography problem that unmixes the shape and albedo from spatially-multiplexed measurements of the NLOS scene. In this paper, we propose a new approach for NLOS imaging by studying the properties of first-returning photons from three-bounce light paths. We show that the times of flight of first-returning photons are dependent only on the geometry of the NLOS scene and each observation is almost always generated from a single NLOS scene point. Exploiting these properties, we derive a space carving algorithm for NLOS scenes. In addition, by assuming local planarity, we derive an algorithm to localize NLOS scene points in 3D and estimate their surface normals. Our methods do not require either the full transient measurements or solving the hard elliptical tomography problem. We demonstrate the effectiveness of our methods through simulations as well as real data captured from a SPAD sensor. This paper presents techniques for tracking non-line-of-sight (NLOS) objects using speckle imaging. We develop a novel speckle formation and motion model where both the sensor and the source view objects only indirectly via a diffuse wall. We show that this NLOS imaging scenario is analogous to direct LOS imaging with the wall acting as a virtual, bare (lens-less) sensor. This enables tracking of a single, rigidly moving NLOS object using existing speckle-based motion estimation techniques. However, when imaging multiple NLOS objects, the speckle components due to different objects are superimposed on the virtual bare sensor image, and cannot be analyzed separately for recovering the motion of individual objects. We develop a novel clustering algorithm based on the statistical and geometrical properties of speckle images, which enables identifying the motion trajectories of multiple, independently moving NLOS objects. We demonstrate, for the first time, tracking individual trajectories of multiple objects around a corner with extreme precision (< 10 microns) using only off-the-shelf imaging components. We explore the question of whether phase-based time-of-flight (TOF) range cameras can be used for looking around corners and through scattering diffusers. By connecting TOF measurements with theory from array signal processing, we conclude that performance depends on two primary factors: camera modulation frequency and the width of the specular lobe (“shininess”) of the wall. For purely Lambertian walls, commodity TOF sensors achieve resolution on the order of meters between targets. For seemingly diffuse walls, such as posterboard, the resolution is drastically reduced, to the order of 10cm. In particular, we find that the relationship between reflectance and resolution is nonlinear—a slight amount of shininess can lead to a dramatic improvement in resolution. Since many realistic scenes exhibit a slight amount of shininess, we believe that off-the-shelf TOF cameras can look around corners. Imaging objects obscured by occluders is a significant challenge for many applications. A camera that could “see around corners” could help improve navigation and mapping capabilities of autonomous vehicles or make search and rescue missions more effective. Time-resolved single-photon imaging systems have recently been demonstrated to record optical information of a scene that can lead to an estimation of the shape and reflectance of objects hidden from the line of sight of a camera. However, existing non-line-of-sight (NLOS) reconstruction algorithms have been constrained in the types of light transport effects they model for the hidden scene parts. We introduce a factored NLOS light transport representation that accounts for partial occlusions and surface normals. Based on this model, we develop a factorization approach for inverse time-resolved light transport and demonstrate high-fidelity NLOS reconstructions for challenging scenes both in simulation and with an experimental NLOS imaging system.