要旨で参照されている関連研究として、multi-fisheye較正の既存パイプライン（joint optimization of intrinsics, extrinsics, and target poses）や、学習ベースのtarget detector、較正の失敗解析に関する研究が挙げられる。具体的な論文名は不明だが、同分野の定番としては、fisheye camera calibrationのための手法（例えば、Kannala-Brandt model）や、multi-camera calibrationのためのツールボックス（例えば、Kalibr）などが関連する。次に読むべき論文は、これらの基盤技術を詳述したものや、較正の失敗解析を扱った最近の研究が考えられる。
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.
