ReviewFrontiers in neurorobotics2019
Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms.
Review in Frontiers in neurorobotics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: A Review.Sensors (Basel, Switzerland) · 2025Review
- Hemispherical Retina Emulated by Plasmonic Optoelectronic Memristors with All-Optical Modulation for Neuromorphic Stereo Vision.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Retina-inspired narrowband perovskite sensor array for panchromatic imaging.Science advances · 2023Article
- An Extended Modular Processing Pipeline for Event-Based Vision in Automatic Visual Inspection.Sensors (Basel, Switzerland) · 2021Article
- A Benchmark Environment for Neuromorphic Stereo Vision.Frontiers in robotics and AI · 2021Article
- Recent advances in infrared imagers: toward thermodynamic and quantum limits of photon sensitivity.Reports on progress in physics. Physical Society (Great Britain) · 2020Article
- A Spike-Based Neuromorphic Architecture of Stereo Vision.Frontiers in neurorobotics · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Any visual sensor, whether artificial or biological, maps the 3D-world on a 2D-representation. The missing dimension is depth and most species use stereo vision to recover it. Stereo vision implies multiple perspectives and matching, hence it obtains depth from a pair of images. Algorithms for stereo vision are also used prosperously in robotics. Although, biological systems seem to compute disparities effortless, artificial methods suffer from high energy demands and latency. The crucial part is the correspondence problem; finding the matching points of two images. The development of event-based cameras, inspired by the retina, enables the exploitation of an additional physical constraint-time. Due to their asynchronous course of operation, considering the precise occurrence of spikes, Spiking Neural Networks take advantage of this constraint. In this work, we investigate sensors and algorithms for event-based stereo vision leading to more biologically plausible robots. Hereby, we focus mainly on binocular stereo vision.
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Registered trials
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