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Found 8 papers, 5 papers with code
An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras
We also demonstrate the integration of image convolution with linear spatial kernels Gaussian, Sobel, and Laplacian as an application of our architecture.
An Asynchronous Kalman Filter for Hybrid Event Cameras
Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes.
Reducing the Sim-to-Real Gap for Event Cameras
We present strategies for improving training data for event based CNNs that result in 20-40% boost in performance of existing state-of-the-art (SOTA) video reconstruction networks retrained with our method, and up to 15% for optic flow networks.
Ranked #3 on
Video Reconstruction
on MVSEC
CED: Color Event Camera Dataset
Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called 'events'.
High Frame Rate Video Reconstruction based on an Event Camera
Based on the abundant event data alongside a low frame rate, easily blurred images, we propose a simple yet effective approach to reconstruct high-quality and high frame rate sharp videos.
Asynchronous Spatial Image Convolutions for Event Cameras
In this paper, we propose a method to compute the convolution of a linear spatial kernel with the output of an event camera.
Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera
In this paper, we propose a simple and effective approach, the \textbf{Event-based Double Integral (EDI)} model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data.
Continuous-time Intensity Estimation Using Event Cameras
Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution.
