Robust Motion Compensation for Event Cameras With Smooth Constraint Event cameras capture brightness changes at an ultrahigh speed and output a series of asynchronous events. In order to form image frames without motion blur from event stream, motion compensation needs to be applied. Unlike other algorithms which either estimate egomotion or estimate optical flow with strict assumptions, we directly align the curved event trajectories with timevarying motion parameters. By maximizing the image energy with smooth constraint on motion parameters, our algorithm can perform accurately and robustly. We conduct extensive experiments on several public datasets and the recently released event camera, i.e. CeleX5, the experimental results verify the superiority of the proposed algorithm.

Learning Eventbased Height from Plane and Parallax Eventbased cameras are a novel asynchronous sensing modality that provide exciting benefits, such as the ability to track fast moving objects with no motion blur and low latency, high dynamic range, and low power consumption. Given the low latency of the cameras, as well as their ability to work in challenging lighting conditions, these cameras are a natural fit for reactive problems such as fast local structure estimation. In this work, we propose a fast method to perform structure estimation for vehicles traveling in a roughly 2D environment (e.g. in an environment with a ground plane). Our method transfers the method of plane and parallax to events, which, given the homography to a ground plane and the pose of the camera, generates a warping of the events which removes the optical flow for events on the ground plane, while inducing flow for events above the ground plane. We then estimate dense flow in this warped space using a selfsupervised neural network, which provides the height of all points in the scene. We evaluate our method on the Multi Vehicle Stereo Event Camera dataset, and show its ability to rapidly estimate the scene structure both at high speeds and in low lighting conditions.