  Event-based cameras are biologically inspired sensors that output
asynchronous pixel-wise brightness changes in the scene called events. They
have a high dynamic range and temporal resolution of a microsecond, opposed to
standard cameras that output frames at fixed frame rates and suffer from motion
blur. Forming stereo pairs of such cameras can open novel application
possibilities, since for each event depth can be readily estimated; however, to
fully exploit asynchronous nature of the sensor and avoid fixed time interval
event accumulation, stereo event lifetime estimation should be employed. In
this paper, we propose a novel method for event lifetime estimation of stereo
event-cameras, allowing generation of sharp gradient images of events that
serve as input to disparity estimation methods. Since a single brightness
change triggers events in both event-camera sensors, we propose a method for
single shot event lifetime and disparity estimation, with association via
stereo matching. The proposed method is approximately twice as fast and more
accurate than if lifetimes were estimated separately for each sensor and then
stereo matched. Results are validated on real-world data through multiple
stereo event-camera experiments.

Robotics;Computer Vision and Pattern Recognition