  Single Photon Avalanche Diode sensor arrays operating in direct time of
flight mode can perform 3D imaging using pulsed lasers. Operating at high frame
rates, SPAD imagers typically generate large volumes of noisy and largely
redundant spatio-temporal data. This results in communication bottlenecks and
unnecessary data processing. In this work, we propose a set of neuromorphic
processing solutions to this problem. By processing the SPAD generated
spatio-temporal patterns locally and in an event-based manner, the proposed
methods reduce the size of output data transmitted from the sensor by orders of
magnitude while increasing the utility of the output data in the context of
challenging recognition tasks. To demonstrate these results, the first large
scale complex SPAD imaging dataset is presented involving high-speed
view-invariant recognition of airplanes with background clutter. The
frame-based SPAD imaging dataset is converted via several alternative methods
into event-based data streams and processed using a range of feature extractor
networks and pooling methods. The results of the event-based processing methods
are compared to processing the original frame-based dataset via frame-based but
otherwise identical architectures. The results show the event-based methods are
superior to the frame-based approach both in terms of classification accuracy
and output data-rate.
