We show that our proposed pipeline provides improved accuracy over the result of the state-of-the-art visual odometry for stereo event-based cameras, while running in real-time on a standard CPU (low-resolution cameras). To the best of our knowledge, this is the first published visual-inertial odometry for stereo event-based cameras.
In this paper, we propose a novel approach by integrating a bio-inspired event camera into the unsupervised video deraining pipeline, which enables us to capture high temporal resolution information and model complex rain characteristics. Specifically, we first design an end-to-end learning-based network consisting of two modules, the asymmetric separation module and the cross-modal fusion module.
Our work has achieved highly consistent outputs with a widely adopted flow cytometer (CytoFLEX) in detecting microparticles. Moreover, the capacity of an event-based photosensor in registering fluorescent signals was evaluated by recording 6 µm Fluorescein isothiocyanate-marked particles in different lighting conditions, revealing superior performance compared to a standard photosensor.
In this paper, we propose the soft epipolar 3D(SEpi-3D) method to eliminate multipath in temporal space with an event camera and a laser projector. Specifically, we align the projector and event camera row onto the same epipolar plane with stereo rectification; we capture event flow synchronized with the projector frame to construct a mapping relationship between event timestamp and projector pixel.
This paper explores the unique membrane potential dynamics of SNNs and their ability to modulate sparse events. We introduce an innovative spike-triggered adaptive threshold mechanism designed for stable training. Building on these insights, we present a specialized spiking feature pyramid network (SpikeFPN) optimized for automotive event-based object detection.