Event-Based Video Reconstruction
6 papers with code • 1 benchmarks • 1 datasets
Event-Based Video Reconstruction aims to generate a sequence of intensity frames from an asynchronous stream of events (per-pixel brightness change signals outputted by an event camera).
Most implemented papers
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.
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.
Event-Based Video Reconstruction Using Transformer
Event cameras, which output events by detecting spatio-temporal brightness changes, bring a novel paradigm to image sensors with high dynamic range and low latency.
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron.
EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction
Event cameras are a new type of vision sensor that incorporates asynchronous and independent pixels, offering advantages over traditional frame-based cameras such as high dynamic range and minimal motion blur.
HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks
Event-based cameras are becoming increasingly popular for their ability to capture high-speed motion with low latency and high dynamic range.