Event Based Vision

Event-based vision utilizes asynchronous data streams from event cameras, which record changes in pixel intensity rather than full frames, to achieve high temporal resolution and dynamic range vision. Current research focuses on developing efficient algorithms and architectures, such as recurrent vision transformers and spiking neural networks, for tasks like object detection, optical flow estimation, and depth estimation, often leveraging self-supervised learning to address data scarcity. This field is significant for its potential to improve robotic perception, autonomous navigation, and other applications requiring low-latency, high-dynamic-range vision in challenging environments.