LECalib: Line-based event camera calibration

Camera calibration is an essential prerequisite for event -based vision applications. Current event camera calibration methods typically involve using flashing patterns, reconstructing intensity images, and utilizing the features extracted from events. Existing methods are generally time-consuming and require manually placed calibration objects, which cannot meet the needs of rapidly changing scenarios. In this paper, we propose a line -based event camera calibration framework exploiting the geometric lines of commonly -encountered objects in man-made environments, e.g., doors, windows, boxes, etc. Different from previous methods, our method detects lines directly from event streams and leverages an event -line calibration model to generate the initial guess of camera parameters, which is suitable for both planar and non -planar lines. Then, a nonlinear optimization is adopted to refine camera parameters. Both simulation and real -world experiments have demonstrated the feasibility and accuracy of our method, with validation performed on monocular and stereo event cameras. The source code is released at https://github.com/Zibin6/line_based_event_camera_calib.