A Spike-Based Neuromorphic Architecture of Stereo Vision

A Spike-Based Neuromorphic Architecture of Stereo Vision

Blog Article

The problem of finding stereo correspondences in binocular vision Amino Acids is solved effortlessly in nature and yet it is still a critical bottleneck for artificial machine vision systems.As temporal information is a crucial feature in this process, the advent of event-based vision sensors and dedicated event-based processors promises to offer an effective approach to solving the stereo matching problem.Indeed, event-based neuromorphic hardware provides an optimal substrate for fast, asynchronous computation, that can make explicit use of precise temporal coincidences.

However, although several biologically-inspired solutions have already been proposed, the performance benefits of combining event-based sensing with asynchronous and parallel computation are yet to be explored.Here we present a hardware spike-based stereo-vision system that leverages the advantages of brain-inspired neuromorphic computing by interfacing two event-based vision sensors to an event-based mixed-signal analog/digital neuromorphic processor.We describe a prototype interface designed to enable the emulation of a stereo-vision system on neuromorphic hardware and we quantify the USB Type-C 14" Dock stereo matching performance with two datasets.

Our results provide a path toward the realization of low-latency, end-to-end event-based, neuromorphic architectures for stereo vision.