  Event cameras are bio-inspired sensors that mimic the human retina by
responding to brightness changes in the scene. They generate asynchronous
spike-based outputs at microsecond resolution, providing advantages over
traditional cameras like high dynamic range, low motion blur and power
efficiency. Most event-based stereo methods attempt to exploit the high
temporal resolution of the camera and the simultaneity of events across cameras
to establish matches and estimate depth. By contrast, this work investigates
how to estimate depth from stereo event cameras without explicit data
association by fusing back-projected ray densities, and demonstrates its
effectiveness on head-mounted camera data, which is recorded in an egocentric
fashion. Code and video are available at https://github.com/tub-rip/dvs_mcemvs
