Imperial College London


Faculty of EngineeringDepartment of Computing

Senior Lecturer



+44 (0)20 7594 7123s.leutenegger Website




360ACE ExtensionSouth Kensington Campus






BibTex format

author = {Bardow, P and Davison, AJ and Leutenegger, S},
doi = {10.1109/CVPR.2016.102},
publisher = {Computer Vision Foundation (CVF)},
title = {Simultaneous Optical Flow and Intensity Estimation from an Event Camera},
url = {},
year = {2016}

RIS format (EndNote, RefMan)

AB - Event cameras are bio-inspired vision sensors whichmimic retinas to measure per-pixel intensity change ratherthan outputting an actual intensity image. This proposedparadigm shift away from traditional frame cameras offerssignificant potential advantages: namely avoiding highdata rates, dynamic range limitations and motion blur.Unfortunately, however, established computer vision algorithmsmay not at all be applied directly to event cameras.Methods proposed so far to reconstruct images, estimateoptical flow, track a camera and reconstruct a scene comewith severe restrictions on the environment or on the motionof the camera, e.g. allowing only rotation. Here, wepropose, to the best of our knowledge, the first algorithm tosimultaneously recover the motion field and brightness image,while the camera undergoes a generic motion throughany scene. Our approach employs minimisation of a costfunction that contains the asynchronous event data as wellas spatial and temporal regularisation within a sliding windowtime interval. Our implementation relies on GPU optimisationand runs in near real-time. In a series of examples,we demonstrate the successful operation of our framework,including in situations where conventional cameras sufferfrom dynamic range limitations and motion blur.
AU - Bardow,P
AU - Davison,AJ
AU - Leutenegger,S
DO - 10.1109/CVPR.2016.102
PB - Computer Vision Foundation (CVF)
PY - 2016///
SN - 1063-6919
TI - Simultaneous Optical Flow and Intensity Estimation from an Event Camera
UR -
UR -
ER -