ArticleFrontiers in neurorobotics2020
A Spike-Based Neuromorphic Architecture of Stereo Vision.
Article in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Spiking neural network decoders of finger forces from high-density intramuscular microelectrode arrays.Nature communications · 2026Article
- A neuromorphic electronic artist for robotic painting.Scientific reports · 2025Article
- Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.Scientific reports · 2025Article
- Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware.Nature communications · 2025Article
- Survey of temporal coding of sensory information.Frontiers in computational neuroscience · 2025Article
- A neuromorphic multi-scale approach for real-time heart rate and state detection.Npj unconventional computing · 2025Article
- Effect of visual perception training on binocular visual function reconstruction in patients after strabismus surgery.International journal of ophthalmology · 2023Article
- Neuromorphic object localization using resistive memories and ultrasonic transducers.Nature communications · 2022Article
- Embodied neuromorphic intelligence.Nature communications · 2022Review
- Robust neuromorphic coupled oscillators for adaptive pacemakers.Scientific reports · 2021Article
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Authors and funding
5 authors.
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Abstract
The problem of finding stereo correspondences in binocular vision 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 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.
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