Passive Ultra-Wideband Single-Photon Imaging

Mian Wei, Sotiris Nousias, Rahul Gulve, David B. Lindell, Kiriakos N. Kutulakos; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 8135-8146

Abstract


We consider the problem of imaging a dynamic scene over an extreme range of timescales simultaneously--seconds to picoseconds--and doing so passively, without much light, and without any timing signals from the light source(s) emitting it. Because existing flux estimation techniques for single-photon cameras break down in this regime, we develop a flux probing theory that draws insights from stochastic calculus to enable reconstruction of a pixel's time-varying flux from a stream of monotonically-increasing photon detection timestamps. We use this theory to (1) show that passive free-running SPAD cameras have an attainable frequency bandwidth that spans the entire DC-to-31 GHz range in low-flux conditions, (2) derive a novel Fourier-domain flux reconstruction algorithm that scans this range for frequencies with statistically-significant support in the timestamp data, and (3) ensure the algorithm's noise model remains valid even for very low photon counts or non-negligible dead times. We show the potential of this asynchronous imaging regime by experimentally demonstrating several never-seen-before abilities: (1) imaging a scene illuminated simultaneously by sources operating at vastly different speeds without synchronization (bulbs, projectors, multiple pulsed lasers), (2) passive non-line-of-sight video acquisition, and (3) recording ultra-wideband video, which can be played back later at 30 Hz to show everyday motions--but can also be played a billion times slower to show the propagation of light itself.

Related Material


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[bibtex]
@InProceedings{Wei_2023_ICCV, author = {Wei, Mian and Nousias, Sotiris and Gulve, Rahul and Lindell, David B. and Kutulakos, Kiriakos N.}, title = {Passive Ultra-Wideband Single-Photon Imaging}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {8135-8146} }