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A deep-learning based high-accuracy camera calibration method for large-scale scene
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AI
DOI：10.1016/j.precisioneng.2024.02.019.png)
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Accurate three-dimensional (3D) measurement for large field of view (FOV) is currently a significant research field. Accordingly, system calibration is crucial to ensure accuracy. However typical calibration methods often involve the use of large calibration objects, which is not only expensive but also difficult to achieve sufficient accuracy. A novel method based on a dual-brand deep neural network (DNN) is proposed for the system calibration. Taking advantage of the concept of divide and conquer, the FOV is divided into sub-regions with a part of overlapping regions by a small calibration object, which forms a large calibration object covering the whole FOV. Then the sub-regions are fused into a global framework and further optimized by the proposed dual-brand DNN. The proposed method reduces the need for calibration objects while improving the calibration accuracy and generalization ability in large FOV. A series of experiments have been designed to prove the effectiveness and robustness of the proposed method.
Keyword:
Camera calibration
Deep neural network
Large-scale scene
Binocular stereo vision
Three-dimensional (3D) metrology
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Improved separated-parameter calibration method for binocular vision measurements with a large field of view.png)
OPTICS EXPRESS
OAAI
OPTICS EXPRESSIF3.3
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