Dynamic Event Camera Calibration

TL;DR

This paper introduces a novel dynamic calibration method for event cameras that calibrates directly from motion-induced events, eliminating the need for static or flashing patterns and enabling quick, reliable calibration in real-world scenarios.

Contribution

It presents the first dynamic event camera calibration algorithm that uses motion to calibrate directly from event data, improving practicality and ease of use.

Findings

Calibration achieved in less than 10 seconds

Method reliably calibrates with motion-based event data

Outperforms static pattern methods in field conditions

Abstract

Camera calibration is an important prerequisite towards the solution of 3D computer vision problems. Traditional methods rely on static images of a calibration pattern. This raises interesting challenges towards the practical usage of event cameras, which notably require image change to produce sufficient measurements. The current standard for event camera calibration therefore consists of using flashing patterns. They have the advantage of simultaneously triggering events in all reprojected pattern feature locations, but it is difficult to construct or use such patterns in the field. We present the first dynamic event camera calibration algorithm. It calibrates directly from events captured during relative motion between camera and calibration pattern. The method is propelled by a novel feature extraction mechanism for calibration patterns, and leverages existing calibration tools before optimizing all parameters through a multi-segment continuous-time formulation. As demonstrated through our results on real data, the obtained calibration method is highly convenient and reliably calibrates from data sequences spanning less than 10 seconds.