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Abstract
Singlephoton lidar has become a prominent tool for depth imaging in recent years. At the core of the technique, the depth of a target is measured by constructing a histogram of time delays between emitted light pulses and detected photon arrivals. A major data processing bottleneck arises on the device when either the number of photons per pixel is large or the resolution of the time stamp is fine, as both the space requirement and the complexity of the image reconstruction algorithms scale with these parameters. We solve this limiting bottleneck of existing lidar techniques by sampling the characteristic function of the time of flight (ToF) model to build a compressive statistic, a socalled sketch of the time delay distribution, which is sufficient to infer the spatial distance and intensity of the object. The size of the sketch scales with the degrees of freedom of the ToF model (number of objects) and not, fundamentally, with the number of photons or the time stamp resolution. Moreover, the sketch is highly amenable for onchip online processing. We show theoretically that the loss of information for compression is controlled and the mean squared error of the inference quickly converges towards the optimal Cram\'erRao bound (i.e. no loss of information) for modest sketch sizes. The proposed compressed singlephoton lidar framework is tested and evaluated on real life datasets of complex scenes where it is shown that a compression rate of upto 1/150 is achievable in practice without sacrificing the overall resolution of the reconstructed image.
Original language  Undefined/Unknown 

Pages  113 
Number of pages  13 
Publication status  Published  17 Feb 2021 
Keywords
 eess.SP
 eess.IV
Projects
 1 Active

Exploiting low dimensional models in sensing, computation and signal processing
1/09/16 → 31/08/21
Project: Research