Corner Occluder Computational Periscopy: Estimating a Hidden Scene from a Single Photograph

The ability to image scenery outside a camera's line-of-sight would be useful in a variety of applications, including autonomous vehicle collision avoidance, or for first responders to anticipate danger around a corner. When a wall obstructs the camera, light cast onto the floor from behind the wall may be used to recover angular variation of light intensity reflected by the hidden scene, forming a 1D scene projection. Recent work has demonstrated that temporal variation in a video, or sequence of floor images, may be used to image moving components of the hidden scene. However, in many applications, it would be useful to be able to image stationary components as well. This earlier approach was also designed for, and tested on, floors that have approximately uniform albedo, while many real floors have spatially varying albedo patterns such as checkered tiles and patterned carpets. In this work, we propose a method to reconstruct a 1D projection of all components in a hidden scene from a single photograph of the floor without assuming uniform floor albedo. Specifically, we derive a forward model that describes the measured photograph as a nonlinear combination of the unknown floor albedo and the light from behind the wall. The inverse problem, which is the joint estimation of floor albedo and a 1D reconstruction of the hidden scene, is then solved via optimization, where we introduce regularizers that help separate light variations in the measured photograph due to floor pattern and hidden scene, respectively. We demonstrate the effectiveness of our formulation and algorithm using synthetic and experimentally measured data.