Count-Free Single-Photon 3D Imaging with Race Logic
Atul Ingle David Maier
Single-photon cameras (SPCs) have emerged as a promising technology for
high-resolution 3D imaging. A single-photon 3D camera determines the round-trip
time of a laser pulse by capturing the arrival of individual photons at each
camera pixel. Constructing photon-timestamp histograms is a fundamental
operation for a single-photon 3D camera. However, in-pixel histogram processing
is computationally expensive and requires large amount of memory per pixel.
Digitizing and transferring photon timestamps to an off-sensor histogramming
module is bandwidth and power hungry. Here we present an online approach for
distance estimation without explicitly storing photon counts. The two key
ingredients of our approach are (a) processing photon streams using race logic,
which maintains photon data in the time-delay domain, and (b) constructing
count-free equi-depth histograms. Equi-depth histograms are a succinct
representation for ``peaky'' distributions, such as those obtained by an SPC
pixel from a laser pulse reflected by a surface. Our approach uses a binner
element that converges on the median (or, more generally, to another quantile)
of a distribution. We cascade multiple binners to form an equi-depth
histogrammer that produces multi-bin histograms. Our evaluation shows that this
method can provide an order of magnitude reduction in bandwidth and power
consumption while maintaining similar distance reconstruction accuracy as
conventional processing methods.