Neuromorphic Monocular Depth Estimation with Uncertainty Modeling

ArXi:2605.10675v1 Announce Type: new Event cameras offer distinct advantages over conventional frame-based sensors, including microsecond-level temporal resolution, high dynamic range, and low bandwidth. In this paper, we predict per-pixel depth distributions from monocular event streams using deep neural networks. We estimate uncertainty using Gaussian, log-normal, and evidential learning frameworks.