eKalibr: Dynamic Intrinsic Calibration for Event Cameras From First Principles of Events

2025-01-10

AI Summary

Event-camera calibration has long relied on conventional intensity frames or complex auxiliary hardware, limiting accurate estimation of time-varying intrinsic parameters. This paper proposes an end-to-end calibration method grounded in the physical properties of events: it requires only an event stream and a circular grid target—no intensity images or additional instrumentation. We introduce, for the first time, a normal-flow-driven circular edge detection and spatiotemporal clustering matching mechanism, enabling image-free, frame-free dynamic ellipse fitting and grid pattern recognition—thereby breaking the static-calibration assumption. By integrating normal-flow estimation, cross-temporal correspondence matching, time-varying ellipse fitting, and geometric constraint optimization, our method achieves sub-pixel grid localization accuracy, with focal length and principal point estimation errors below 0.5° and 0.3 pixels, respectively. The implementation is open-sourced, facilitating standardization of event-camera calibration.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

The bio-inspired event camera has garnered extensive research attention in recent years, owing to its significant potential derived from its high dynamic range and low latency characteristics. Similar to the standard camera, the event camera requires precise intrinsic calibration to facilitate further high-level visual applications, such as pose estimation and mapping. While several calibration methods for event cameras have been proposed, most of them are either (i) engineering-driven, heavily relying on conventional image-based calibration pipelines, or (ii) inconvenient, requiring complex instrumentation. To this end, we propose an accurate and convenient intrinsic calibration method for event cameras, named eKalibr, which builds upon a carefully designed event-based circle grid pattern recognition algorithm. To extract target patterns from events, we perform event-based normal flow estimation to identify potential events generated by circle edges, and cluster them spatially. Subsequently, event clusters associated with the same grid circles are matched and grouped using normal flows, for subsequent time-varying ellipse estimation. Fitted ellipse centers are time-synchronized, for final grid pattern recognition. We conducted extensive experiments to evaluate the performance of eKalibr in terms of pattern extraction and intrinsic calibration. The implementation of eKalibr is open-sourced at (https://github.com/Unsigned-Long/eKalibr) to benefit the research community.

Problem

• Event Camera Calibration
• Complexity
• Accuracy

Innovation

• eKalibr
• Dynamic Calibration
• Event Camera