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

TL;DR

eKalibr introduces an intrinsic calibration method for event cameras that leverages event-based circle grid pattern recognition. By constructing a surface of active events, estimating normal flows, clustering and matching run-chase edge clusters, and fitting time-varying ellipses to circle centers, it extracts synchronized grid patterns from raw events for calibration. The approach demonstrates competitive accuracy and repeatability relative to frame-based methods while offering higher practicality than LED-only or image-reconstruction pipelines, and it is openly available as open-source software. The practical impact lies in enabling accurate camera calibration in dynamic, high-dynamic-range environments without requiring complex hardware or frame-based reconstructions.

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.

Figures (9)

• Figure 1: The runtime visualization of circle grid pattern recognition in eKalibr. eKalibr extracts patterns from raw events in the spatiotemporal domain from first principles of events.
• Figure 2: Illustration of the pipeline of the proposed event-based visual intrinsic calibration method. A detailed description of the pipeline is provided in Section \ref{['sect:overview']}, while detailed methodology is presented in Section \ref{['sect:nf_est']}, Section \ref{['sect:ellipse_est']}, and Section \ref{['sect:intr_est']}.
• Figure 3: Schematic of event clustering. Subfigure A: events with high (green) and low (red) inlier rates in the plane fitting. Subfigure B: the estimated normal flows (green lines). Subfigure C: inlier events, blue ones are positive events (C2) while red ones are negative events (C1). Subfigure D: clustering results, different colors (randomly generated) represent distinct clusters.
• Figure 4: Illustration of the normal flow distribution of edges of grid circles in the image plane. The relative motion between the circle and the camera results in two types of events generated by edges, which exhibit significant distinguishability regarding the directions of the normal flows.
• Figure 5: Schematic of cluster type identification (left) and matching (right).