Occlusion-based computational periscopy with consumer cameras

John Murray-Bruce; Charles Saunders; Vivek K. Goyal

doi:10.1117/12.2528322

9 September 2019 Occlusion-based computational periscopy with consumer cameras

John Murray-Bruce, Charles Saunders, Vivek K. Goyal

Proceedings Volume 11138, Wavelets and Sparsity XVIII; 111380X (2019) https://doi.org/10.1117/12.2528322
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

The ability to form images of scenes hidden from direct view would be advantageous in many applications – from improved motion planning and collision avoidance in autonomous navigation to enhanced danger anticipation for first-responders in search-and-rescue missions. Recent techniques for imaging around corners have mostly relied on time-of-flight measurements of light propagation, necessitating the use of expensive, specialized optical systems. In this work, we demonstrate how to form images of hidden scenes from intensity-only measurements of the light reaching a visible surface from the hidden scene. Our approach exploits the penumbra cast by an opaque occluding object onto a visible surface. Specifically, we present a physical model that relates the measured photograph to the radiosity of the hidden scene and the visibility function due to the opaque occluder. For a given scene–occluder setup, we characterize the parts of the hidden region for which the physical model is well-conditioned for inversion – i.e., the computational field of view (CFOV) of the imaging system. This concept of CFOV is further verified through the Cram´er–Rao bound of the hidden-scene estimation problem. Finally, we present a two-step computational method for recovering the occluder and the scene behind it. We demonstrate the effectiveness of the proposed method using both synthetic and experimentally measured data.

Citation Download Citation

John Murray-Bruce, Charles Saunders, and Vivek K. Goyal "Occlusion-based computational periscopy with consumer cameras", Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380X (9 September 2019); https://doi.org/10.1117/12.2528322

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