Bi-Modal Compositional Network For Feature Disentanglement

Time-of-Flight (Tof) imagers, e.g. Microsoft Kinect, are active devices that offer a portable, efficient and a consumer-grade solution to three dimensional imaging problem. As the name suggests, in Tof imaging, back scattered light from an active illumination source (typically a sinusoid) is used to measure the Tof, thus resulting in depth information. Despite its prevalence in applications such as autonomous navigation and scientific imaging, current Tof sensors are limited in their dynamic range. Computational imaging solutions enabling high dynamic range (HDR) Tof imaging are largely unexplored. We take a step in this direction by proposing a novel architecture for HDR Tof imaging; we combine Tof imaging with the recently introduced Unlimited Sensing Framework. By considering modulo sampling at each Tof pixel, HDR signals are folded back in the conventional dynamic range. Our work offers a single-shot solution for HDR Tof imaging. We report a sampling density criterion that guarantees inversion of modulo non-linearity. Furthermore, we also present a new algorithm for Tof recovery that circumvents the need for unfolding of modulo samples. Numerical examples based on the Stanford 3D Scanning Repository highlight the merits of our approach, thus paving a path for a novel imaging architecture.