EventDC uses event-stream-conditioned dynamic convolutions (EMA and LDF modules) to align and refine RGB and sparse-depth features, achieving state-of-the-art results on a new event-based depth completion benchmark.
Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion
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abstract
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
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Event-Driven Dynamic Scene Depth Completion
EventDC uses event-stream-conditioned dynamic convolutions (EMA and LDF modules) to align and refine RGB and sparse-depth features, achieving state-of-the-art results on a new event-based depth completion benchmark.