Neural events compress event camera streams into fewer informative tokens via discrete asynchronous autoencoders, achieving on-par or better performance on detection and classification with 2x lower event rate.
Are high-resolution event cameras really needed?
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
EvSLAM is a new benchmark for event-based state estimation that exposes limitations in current methods and datasets for high-speed 6-DoF maneuvers using diverse platforms, extreme lighting, and a custom evaluation metric.
STGDNet uses synchronized spatial and temporal difference data from the Tianmouc sensor within a recurrent fusion architecture to deblur RGB frames more effectively than prior RGB-only or event-camera methods.
citing papers explorer
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Neural Events: Discrete Asynchronous Autoencoders for Event-Based Vision
Neural events compress event camera streams into fewer informative tokens via discrete asynchronous autoencoders, achieving on-par or better performance on detection and classification with 2x lower event rate.
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Event-based SLAM Benchmark for High-Speed Maneuvers
EvSLAM is a new benchmark for event-based state estimation that exposes limitations in current methods and datasets for high-speed 6-DoF maneuvers using diverse platforms, extreme lighting, and a custom evaluation metric.
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Spatio-Temporal Difference Guided Motion Deblurring with the Complementary Vision Sensor
STGDNet uses synchronized spatial and temporal difference data from the Tianmouc sensor within a recurrent fusion architecture to deblur RGB frames more effectively than prior RGB-only or event-camera methods.