Feature Extraction Reimagined: Achieving Superior Accuracy in Camera Calibration

• URL: http://arxiv.org/abs/2410.13371v2
• Date: Fri, 15 Nov 2024 03:07:13 GMT
• Title: Feature Extraction Reimagined: Achieving Superior Accuracy in Camera Calibration
• Authors: Zezhun Shi,
• Abstract summary: This paper focuses on improving the accuracy of feature extraction, which is a key step in calibration. We introduce a novel dynamic calibration target that synthesizes multiple checkerboard patterns of different angle around pattern center. We also propose a novel cost function of feature refinement that accounts for defocus effect, offering a more physically realistic model.
• Score: 0.0
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• Abstract: Camera calibration is crucial for 3D vision applications. This paper focuses on improving the accuracy of feature extraction, which is a key step in calibration. We address the aliasing problem of star-shaped pattern by introducing a novel dynamic calibration target that synthesizes multiple checkerboard patterns of different angle around pattern center, which significantly improves feature refinement accuracy. Additionally, we propose a novel cost function of feature refinement that accounts for defocus effect, offering a more physically realistic model compared to existing symmetry based method, experiment on a large dataset demonstrate significant improvements in calibration accuracy with reduced computation time. Our code is available from https://github.com/spdfghi/Feature-Extraction-Reimagined-Achieving-Superior-Accuracy-in-Camera-Calib ration.git.

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