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The stereo-matching problem, i.e., matching corresponding features in twodifferent views to reconstruct depth, is efficiently solved in biology. Yet, itremains the computational bottleneck for classical machine vision approaches.By exploiting the properties of event cameras, recently proposed Spiking NeuralNetwork (SNN) architectures for stereo vision have the potential of simplifyingthe stereo-matching problem. Several solutions that combine event cameras withspike-based neuromorphic processors already exist. However, they are eithersimulated on digital hardware or tested on simplified stimuli. In this work, weuse the Dynamic Vision Sensor 3D Human Pose Dataset (DHP19) to validate abrain-inspired event-based stereo-matching architecture implemented on amixed-signal neuromorphic processor with real-world data. Our experiments showthat this SNN architecture, composed of coincidence detectors and disparitysensitive neurons, is able to provide a coarse estimate of the input disparityinstantaneously, thereby detecting the presence of a stimulus moving in depthin real-time.

Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup