CalibAnyView: Beyond Single-View Camera Calibration in the Wild

• URL: http://arxiv.org/abs/2605.14615v1
• Date: Thu, 14 May 2026 09:32:12 GMT
• Title: CalibAnyView: Beyond Single-View Camera Calibration in the Wild
• Abstract summary: Camera calibration is a fundamental prerequisite for reliable geometric perception.<n>Recent learning-based methods have shown promising results for single-view calibration, but inherently neglect geometric consistency across multiple views.<n>We introduce CalibAnyView, a unified formulation that supports an arbitrary number of input views.
• Score: 59.66873936532375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Camera calibration is a fundamental prerequisite for reliable geometric perception, yet classical approaches rely on controlled acquisition setups that are impractical for in-the-wild imagery. Recent learning-based methods have shown promising results for single-view calibration, but inherently neglect geometric consistency across multiple views. We introduce CalibAnyView, a unified formulation that supports an arbitrary number of input views ($N \geq 1$) by explicitly modeling cross-view geometric consistency. To facilitate this, we construct a large-scale multi-view video dataset covering diverse real-world scenarios, including multiple camera models, dynamic scenes, realistic motion trajectories, and heterogeneous lens distortions. Building on this dataset, we develop a multi-view transformer that predicts dense perspective fields, which are further integrated into a geometric optimization framework to jointly estimate camera intrinsics and gravity direction. Extensive experiments demonstrate that CalibAnyView consistently outperforms state-of-the-art methods, achieves strong robustness under single-view settings, and further improves with multi-view inference, providing a reliable foundation for downstream tasks such as 3D reconstruction and robotic perception in the wild.

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