Event-based vision
34 papers with code • 1 benchmarks • 9 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Datasets
N-ImageNet
CED
COESOT
TUM-VIE
Event-Human3.6m
Most implemented papers
Event-based Vision: A Survey
uzh-rpg/event-based_vision_resources
•
•
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-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.
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 Cameras, Contrast Maximization and Reward Functions: An Analysis
TimoStoff/events_contrast_maximization
•
•
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.
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.
A Large Scale Event-based Detection Dataset for Automotive
We introduce the first very large detection dataset for event cameras.
Lifting Monocular Events to 3D Human Poses
IIT-PAVIS/lifting_events_to_3d_hpe
•
•
Here we propose the first learning-based method for 3D human pose from a single stream of events.
Moving Object Detection for Event-based Vision using k-means Clustering
Moving object detection is important in computer vision.
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
However, event-based vision has been held back by the shortage of labeled datasets due to the novelty of event cameras.
