Abstract

The conventional checkerboard-based calibration for standard cameras faces fundamental limitations when applied to bio-inspired event cameras. Specifically, this stems from two challenges: (i) Events are triggered asynchronously at different timestamps along motion trajectories. If we accumulate them directly on the image plane, it causes temporal misalignment and produces blurred edges. (ii) Checkerboard corners on event cameras show near-zero event occurrence at the corner itself. This hinders reliable corner localization and makes calibration difficult. To address these issues, we present a novel calibration framework that directly detects checkerboard corners from event data without learning-based grayscale image reconstruction. We first mathematically analyze the absence of events at corner points. Based on this fact, we then leverage edge-driven event cues to initialize corner positions. Using the nearzero event occurrence at checkerboard corners, we gradually refine the estimated corner toward low event-density regions, achieving sub-pixel accuracy. Furthermore, we extend the corner detection to fiducial markers such as AprilTag, resulting in reliable detection even under partial visibility or occlusion. Evaluations on self-collected and public data demonstrate reliable checkerboard corner detection and stable camera calibration.

A research team affiliated with UNIST has introduced a new calibration technique for event cameras—an essential sensor for high-speed robotics and autonomous vehicles. Unlike conventional methods, this approach uses standard checkerboard patterns to calibrate the sensors directly from event data, eliminating the need for image reconstruction.

Event cameras detect only changes in brightness at individual pixels, enabling rapid perception in challenging conditions, such as low light or fast motion. However, calibrating these sensors—particularly with common checkerboard targets—has been problematic because the key points at the intersections of black and white squares rarely produce detectable events.

Led by Professor Kyungdon Joo from UNIST's Graduate School of Artificial Intelligence, the team developed a computer vision approach that bypasses this challenge. Instead of locating checkerboard corners directly within event data, the method first detects the pattern's boundary lines. It then identifies the corners as intersections where these lines meet and where minimal activity occurs—since brightness changes cancel out at intersections. This insight, grounded in the mathematical behavior of event signals, enables precise corner detection without converting event data into traditional images.

The team also improved the clarity of the detected grid. Because event signals are recorded asynchronously across pixels, slight movements can cause the pattern to blur. Their technique aligns and refines these signals, reconstructing sharp grid lines and significantly enhancing calibration accuracy.

Furthermore, the method extends to AprilTags—square fiducial markers similar to QR codes used for localization. It can identify and decode these tags solely from event data, even when some are partially obscured or outside the camera's field of view.

First author Taehun Ryu explains, “Previous methods had to convert event data into grayscale images to find corners, which could introduce blurring and errors. Our approach directly extracts reference points from raw signals, greatly improving calibration precision.”

Professor Joo highlights the broader significance, “Accurate camera calibration is fundamental to many vision systems. Our work paves the way for deploying event cameras in real-world robots and vehicles.”

The study has been selected as a Highlight at the 2026 Conference on Computer Vision and Pattern Recognition (CVPR), scheduled for June 3–7 in Denver, USA. Only about 3.5% of submissions earn this recognition, which honors outstanding contributions to the field. The research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP), the Ministry of Science and ICT (MSIT), the National Research Foundation of Korea (NRF), and UNIST's Graduate School of Artificial Intelligence. Additional support came from projects including the Development of AI Bots Collaboration Platform and Self-organizing and the Geometric and Physical Commonsense Reasoning based Behavior Intelligence for Embodied AI . 

Journal Reference

Taehun Ryu, Changwoo Kang, and Kyungdon Joo, "From Corners to Fiducial Tags: Revisiting Checkerboard Calibration for Event Cameras,"    '26        CVPR,  (2026).