Asynchronous event-based sensors present new challenges in basic robot vision problems
like feature tracking. The few existing approaches rely on grouping events into models and …

We present an unsupervised learning pipeline for dense depth, optical flow and egomotion
estimation for autonomous driving applications, using the event-based output of the …

Event-based sensing, ie, the asynchronous detection of luminance changes, promises low-
energy, high dynamic range, and sparse sensing. This stands in contrast to whole image …

In this work we present a lightweight, unsupervised learning pipeline for\textit {dense} depth,
optical flow and egomotion estimation from sparse event output of the Dynamic Vision …

Moving-objects detection is a critical ability for an autonomous vehicle. Facing the high
detection requirements and the slow target-extraction problem of a common camera, this …

Segmentation of moving objects in dynamic scenes is a key process in scene understanding
for both navigation and video recognition tasks. Without prior knowledge of the object …

Calibration is an important step before fusing the data of multi-sensors together. The
methods which had been proposed to calibrate a regular camera and 3D LiDAR cannot be …

Event-based sensing, ie the asynchronous detection of luminance changes, promises low-
energy, high dynamic range, and sparse sensing. This stands in contrast to whole image …

In this work, we propose a two-layered descriptive model for motion processing from retina
to the cortex, with an event-based input from the asynchronous time-based image sensor …

Biologically inspired computational models of visual processing often utilize conventional
frame-based cameras for data acquisition. Instead, the Dynamic Vision Sensor (DVS) …