A receding-horizon MLE recovers Neural-ODE parameters and event thresholds from event camera data by modeling events as a history-dependent marked point process.
A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation
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Receding-Horizon Maximum-Likelihood Estimation of Neural-ODE Dynamics and Thresholds from Event Cameras
A receding-horizon MLE recovers Neural-ODE parameters and event thresholds from event camera data by modeling events as a history-dependent marked point process.