Observation Quality Matters: Robust Multi-Fisheye Calibration via Failure-Oriented Analysis
Multi-fisheye camera calibration is challenging as rig size and field of view increase. This paper reveals through failure-oriented analysis that intrinsic initialization is the dominant failure factor and proposes CO-Calib, a plug-in framework that improves success rate from 68.1% to 99.3%.
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[Submitted on 7 Jul 2026]
Title:Observation Quality Matters: Robust Multi-Fisheye Calibration via Failure-Oriented Analysis
View a PDF of the paper titled Observation Quality Matters: Robust Multi-Fisheye Calibration via Failure-Oriented Analysis, by Peize Liu and 3 other authors
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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 this https URL.
Comments: 9 pages, 7 figures, 6 tables. Code: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.05777 [cs.RO]
(or arXiv:2607.05777v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.05777
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chen Feng [view email] [v1] Tue, 7 Jul 2026 03:07:23 UTC (18,690 KB)
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