Compressed gated range sensing

Abstract

Active Range Imaging (ARI) has recently sparked an enthusiastic interest due to the numerous applications that can benefit from the high quality depth maps that ARI systems offer. One of the most successful ARI techniques employs Time-of-Flight (ToF) cameras which emit and subsequently record laser pulses in order to estimate the distance between the camera and objects in a scene. A limitation of this type of ARI is the requirement for a large number of frames that have to be captured in order to generate high resolution depth maps. In this work, we introduce Compressed Gated Range Sensing (CGRS), a novel approach for ToF-based ARI that utilizes the recently proposed framework of Compressed Sensing (CS) to dramatically reduce the number of necessary frames. The CGRS technique employs a random gating function along with state-of-the-art reconstruction in order to estimate the timing of a returning laser pulse and infer the depth map. To validate our method, software simulations were carried out using a realistic system model. Simulated results suggest that low error reconstruction of a depth map is possible using approximately 20% of the frames that traditional ToF cameras require, while 30% sampling rates can achieve very high fidelity reconstruction.

Recent advances in transient imaging: A computer graphics and vision perspective

“…In order to improve convergence on range imaging (i.e. focusing on single scattering), approaches such as rangegates coding were developed, where the gate response is continuously modulated over a long time window, then reconstructed using intensity analysis [Laurenzis et al 2007;Zhang and Yan 2011;Laurenzis and Bacher 2011], compressed sensing (CS) on the temporal domain by random temporal gating [Li et al 2012;Tsagkatakis et al 2012;Tsagkatakis et al 2013;Tsagkatakis et al 2015], or hybrid approaches combining both techniques [Zhang et al 2012;Dai et al 2013]. This can reduce the number of measurements to just two, for 13-bit range images.…”

Recovery of quantized compressed sensing measurements

“…Compressed Sensing (CS) 1, 2 is a novel mathematical framework that has revolutionized modern signal and image acquisition architectures ranging from Single Pixel Cameras 3 and CS based spectral imaging 4 , to range sensing 5 and Ultrasound imaging 6 . In imaging scenarios where hard constrains are imposed, including spatial, temporal and spectral resolution, power consumption, and robustness, CS based architectures outperform traditional approaches, both from a theoretical as well as a practical standpoint.…”