Event-based vision
53 papers with code • 1 benchmarks • 11 datasets
An event camera, also known as a neuromorphic camera, silicon retina or dynamic vision sensor, is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur and staying silent otherwise. Modern event cameras have microsecond temporal resolution, 120 dB dynamic range, and less under/overexposure and motion blur than frame cameras.
Benchmarks
These leaderboards are used to track progress in Event-based vision
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Libraries
Use these libraries to find Event-based vision models and implementationsDatasets
TUM-VIE
CED
N-ImageNet
COESOT
Event-Human3.6m
Most implemented papers
Event-based Camera Pose Tracking using a Generative Event Model
Event-based vision sensors mimic the operation of biological retina and they represent a major paradigm shift from traditional cameras.
Person Re-Identification without Identification via Event Anonymization
IIT-PAVIS/ReId_without_Id
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ICCV 2023
In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach.
GET: Group Event Transformer for Event-Based Vision
peterande/get-group-event-transformer
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ICCV 2023
Event cameras are a type of novel neuromorphic sen-sor that has been gaining increasing attention.
Event-based Background-Oriented Schlieren
uzh-rpg/event-based_vision_resources
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Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding.
State Space Models for Event Cameras
uzh-rpg/ssms_event_cameras
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CVPR 2024
We address this challenge by introducing state-space models (SSMs) with learnable timescale parameters to event-based vision.
eCARLA-scenes: A synthetically generated dataset for event-based optical flow prediction
We further present a synthetic event-based datasets and data generation pipelines for optical flow prediction tasks.
Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception
Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer.
Focus Is All You Need: Loss Functions For Event-based Vision
The proposed loss functions allow bringing mature computer vision tools to the realm of event cameras.
Event-based Vision: A Survey
uzh-rpg/event-based_vision_resources
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Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur.
Event Cameras, Contrast Maximization and Reward Functions: An Analysis
TimoStoff/events_contrast_maximization
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CVPR 2019
The versatility of this approach has lead to a flurry of research in recent years, but no in-depth study of the reward chosen during optimization has yet been made.
