  The stereo-matching problem, i.e., matching corresponding features in two
different views to reconstruct depth, is efficiently solved in biology. Yet, it
remains the computational bottleneck for classical machine vision approaches.
By exploiting the properties of event cameras, recently proposed Spiking Neural
Network (SNN) architectures for stereo vision have the potential of simplifying
the stereo-matching problem. Several solutions that combine event cameras with
spike-based neuromorphic processors already exist. However, they are either
simulated on digital hardware or tested on simplified stimuli. In this work, we
use the Dynamic Vision Sensor 3D Human Pose Dataset (DHP19) to validate a
brain-inspired event-based stereo-matching architecture implemented on a
mixed-signal neuromorphic processor with real-world data. Our experiments show
that this SNN architecture, composed of coincidence detectors and disparity
sensitive neurons, is able to provide a coarse estimate of the input disparity
instantaneously, thereby detecting the presence of a stimulus moving in depth
in real-time.
