Observation Quality Matters: Robust Multi-Fisheye Calibration via Failure-Oriented Analysis

2026-07-06

AI Summary

This work addresses the frequent failure of multi-fisheye camera calibration due to poor initial intrinsic estimates, particularly under complex layouts and large fields of view where observation quality is degraded. The authors propose CO-Calib, a novel framework that reveals— for the first time—that limited radial span in observed features induces coupling between focal length and fisheye distortion parameters, leading to ill-conditioned updates. To mitigate this, they introduce a plug-and-play calibration data construction strategy integrating learning-based target detection, error-aware frame selection, multi-camera co-visibility constraints, and robust initialization. Experiments demonstrate that the method boosts calibration success rates from 68.1% to 99.3% on both synthetic and real multi-fisheye systems, substantially improving extrinsic accuracy and overall calibration stability.

Abstract

Reliable calibration of multi-fisheye camera systems remains challenging as rig size, camera arrangement diversity, and field of view increase. Existing pipelines can jointly optimize intrinsics, extrinsics, and target poses, but their success still depends heavily on empirical capture rules and the quality of the observations supplied to the solver. This paper studies this dependency through a failure-oriented analysis. We reveal that calibration failures are not sufficiently explained by detector recall loss or global image-plane distribution imbalance. Instead, the dominant failure factor lies in intrinsic initialization: observations with limited radial span couple focal scale with fisheye projection-shape parameters, producing ill-conditioned updates. Guided by this insight, we propose CO-Calib, a plug-in calibration-data construction framework that combines a robust learning-based target detector with an error-analysis-guided frame selector. CO-Calib constructs initialization-friendly anchors, co-visible multi-camera constraints, and coverage-completion frames without changing the existing calibration workflow or optimization backend. Extensive experiments on synthetic and real multi-fisheye systems demonstrate that CO-Calib improves the overall success rate from 68.1% to 99.3%, increases extrinsic accuracy, and augments real-world calibration stability. The source code will be made publicly available at https://github.com/HKUST-Aerial-Robotics/CO-Calib.

Problem

• multi-fisheye calibration
• observation quality
• calibration robustness
• intrinsic initialization
• failure analysis

Innovation

• multi-fisheye calibration
• failure-oriented analysis
• initialization robustness
• coverage-aware frame selection
• plug-in calibration framework