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Event cameras are bio-inspired sensors that mimic the human retina byresponding to brightness changes in the scene. They generate asynchronousspike-based outputs at microsecond resolution, providing advantages overtraditional cameras like high dynamic range, low motion blur and powerefficiency. Most event-based stereo methods attempt to exploit the hightemporal resolution of the camera and the simultaneity of events across camerasto establish matches and estimate depth. By contrast, this work investigateshow to estimate depth from stereo event cameras without explicit dataassociation by fusing back-projected ray densities, and demonstrates itseffectiveness on head-mounted camera data, which is recorded in an egocentricfashion. Code and video are available at https://github.com/tub-rip/dvs_mcemvs

Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion