Robust Multi-view Camera Calibration from Dense Matches

• URL: http://arxiv.org/abs/2512.15608v1
• Date: Wed, 17 Dec 2025 17:19:36 GMT
• Title: Robust Multi-view Camera Calibration from Dense Matches
• Authors: Johannes Hägerlind, Bao-Long Tran, Urs Waldmann, Per-Erik Forssén,
• Abstract summary: We introduce a robust method for pose estimation and calibration.<n>We consider a set of rigid cameras, each observing the scene from a different perspective.<n>Specifically, we analyse the individual components in a structure-from-motion (SfM) pipeline.
• Score: 1.6336895015108397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating camera intrinsics and extrinsics is a fundamental problem in computer vision, and while advances in structure-from-motion (SfM) have improved accuracy and robustness, open challenges remain. In this paper, we introduce a robust method for pose estimation and calibration. We consider a set of rigid cameras, each observing the scene from a different perspective, which is a typical camera setup in animal behavior studies and forensic analysis of surveillance footage. Specifically, we analyse the individual components in a structure-from-motion (SfM) pipeline, and identify design choices that improve accuracy. Our main contributions are: (1) we investigate how to best subsample the predicted correspondences from a dense matcher to leverage them in the estimation process. (2) We investigate selection criteria for how to add the views incrementally. In a rigorous quantitative evaluation, we show the effectiveness of our changes, especially for cameras with strong radial distortion (79.9% ours vs. 40.4 vanilla VGGT). Finally, we demonstrate our correspondence subsampling in a global SfM setting where we initialize the poses using VGGT. The proposed pipeline generalizes across a wide range of camera setups, and could thus become a useful tool for animal behavior and forensic analysis.

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