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
翻译:事件摄像头是生物感应传感器,通过对现场亮度变化作出反应来模仿人类视网膜,在微二分解时产生无同步的钉钉结果,对高动态范围、低运动模糊度和功率等传统照相机提供优势,大多数事件立体法都试图利用相机的高时间分辨率和各种摄像头事件的同时性来建立匹配和估计深度。 与此相反,这项工作通过使用反射射密度来调查如何通过没有明确数据关联的立体事件摄像头摄像头来估计深度,并展示其在头挂相机数据上的有效性,这些数据以自我中心的方式记录。