How to Calibrate Your Event Camera

TL;DR

This paper introduces a neural-network-based calibration method for event cameras that uses image reconstruction, allowing calibration with standard patterns and enabling calibration between frame-based and event-based sensors without extra complexity.

Contribution

It presents a novel calibration framework leveraging image reconstruction, eliminating the need for active illumination patterns and facilitating calibration between different sensor types.

Findings

Calibration accuracy is maintained under common distortion models.

The method works effectively in both simulation and real-world scenarios.

It enables calibration without active illumination or complex setups.

Abstract

We propose a generic event camera calibration framework using image reconstruction. Instead of relying on blinking LED patterns or external screens, we show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras. The advantage of our proposed approach is that we can use standard calibration patterns that do not rely on active illumination. Furthermore, our approach enables the possibility to perform extrinsic calibration between frame-based and event-based sensors without additional complexity. Both simulation and real-world experiments indicate that calibration through image reconstruction is accurate under common distortion models and a wide variety of distortion parameters