NERVE is a new 600GB multi-sensor dataset with DVS, RGB-D, and 24/77GHz radar plus baselines showing DVS+77GHz radar fusion improves human detection to 47.5% mAP with sub-1.8m distance error.
Event- based vision: A survey
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7representative citing papers
Event-camera tracking of propeller frequencies and ellipse fitting yields under 3% frequency error on five real outdoor quadrotor flights and supplies thrust and tilt inputs for relative state estimation.
Hardware-aware pruning plus quantization of EFGCN models cuts BRAM use 26–31% across three event datasets with 1.65–5.18% accuracy loss, validated by a ZCU104 proof-of-concept.
An end-to-end spiking encoder-decoder network achieves 92.05/87.04/86.51 AP on KITTI BEV detection with a claimed 3.33x synaptic energy reduction versus an equivalent CNN.
A neuromorphic event-based tactile pipeline with spatiotemporal segmentation and ResNet achieves >=98% character accuracy and >90% word accuracy for continuous Braille reading on physical boards.
A keypoint-based pipeline extracts and tracks points from event streams to compute accurate 6-DoF poses of moving objects, outperforming prior event-based methods in simulated and real tests.
A single-qubit quantum spiking RNN is applied to continuous-valued forecasting and claims a 15.4% MSE gain over a classical LIF baseline, but the comparison does not isolate quantum effects.
citing papers explorer
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NERVE: A Neuromorphic Vision and Radar Ensemble for Multi-Sensor Fusion Research
NERVE is a new 600GB multi-sensor dataset with DVS, RGB-D, and 24/77GHz radar plus baselines showing DVS+77GHz radar fusion improves human detection to 47.5% mAP with sub-1.8m distance error.
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Relative State Estimation using Event-Based Propeller Sensing
Event-camera tracking of propeller frequencies and ellipse fitting yields under 3% frequency error on five real outdoor quadrotor flights and supplies thrust and tilt inputs for relative state estimation.
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Hardware-aware Graph Neural Networks prunning for embedded event-based vision
Hardware-aware pruning plus quantization of EFGCN models cuts BRAM use 26–31% across three event datasets with 1.65–5.18% accuracy loss, validated by a ZCU104 proof-of-concept.
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Neuromorphic LiDAR-based Bird's Eye View Object Detection using Energy-efficient Spiking Neural Networks
An end-to-end spiking encoder-decoder network achieves 92.05/87.04/86.51 AP on KITTI BEV detection with a claimed 3.33x synaptic energy reduction versus an equivalent CNN.
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Neuromorphic BrailleNet: Accurate and Generalizable Braille Reading Beyond Single Characters through Event-Based Optical Tactile Sensing
A neuromorphic event-based tactile pipeline with spatiotemporal segmentation and ResNet achieves >=98% character accuracy and >90% word accuracy for continuous Braille reading on physical boards.
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Keypoint-based Dynamic Object 6-DoF Pose Tracking via Event Camera
A keypoint-based pipeline extracts and tracks points from event streams to compute accurate 6-DoF poses of moving objects, outperforming prior event-based methods in simulated and real tests.
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QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting
A single-qubit quantum spiking RNN is applied to continuous-valued forecasting and claims a 15.4% MSE gain over a classical LIF baseline, but the comparison does not isolate quantum effects.