A calibration scheme for non-line-of-sight imaging setups.

The recent years have given rise to a large number of techniques for "looking around corners", i.e., for reconstructing or tracking occluded objects from indirect light reflections off a wall.

SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence.

Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in VR/AR settings, has seen tremendous progress in recent years. The combination of both these components into telepresence systems, however, comes with significant technical challenges. All approaches proposed so far are extremely demanding on input and output devices, compute resources and transmission bandwidth, and they do not reach the level of immediacy required for applications such as remote collaboration.

Neural network identification of people hidden from view with a single-pixel, single-photon detector.

Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with a deep convolutional artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N = 3).

Tracking objects outside the line of sight using 2D intensity images.

The observation of objects located in inaccessible regions is a recurring challenge in a wide variety of important applications. Recent work has shown that using rare and expensive optical setups, indirect diffuse light reflections can be used to reconstruct objects and two-dimensional (2D) patterns around a corner. Here we show that occluded objects can be tracked in real time using much simpler means, namely a standard 2D camera and a laser pointer.

Resolving transient time profile in ToF imaging via log-sum sparse regularization.

Multi-frequency time-of-flight (ToF) cameras have been used to recover the transient time profiles of optical responses such that multipath interference can be separated. The resolution of the recovered time profiles is limited by the highest modulation frequency. Here, we demonstrate a method based on log-sum sparsity regularization to recover transient time profiles of specular reflections.

Imaging in scattering media using correlation image sensors and sparse convolutional coding.

Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media.