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Camera calibration and 3D reconstruction (calib3d module) {#tutorial_table_of_content_calib3d}
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==========================================================
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Although we got most of our images in a 2D format they do come from a 3D world. Here you will learn
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how to find out from the 2D images information about the 3D world.
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- @subpage tutorial_camera_calibration_square_chess
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*Compatibility:* \> OpenCV 2.0
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*Author:* Victor Eruhimov
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You will use some chessboard images to calibrate your camera.
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- @subpage tutorial_camera_calibration
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*Compatibility:* \> OpenCV 2.0
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*Author:* Bernát Gábor
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Camera calibration by using either the chessboard, circle or the asymmetrical circle
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pattern. Get the images either from a camera attached, a video file or from an image
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collection.
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- @subpage tutorial_real_time_pose
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*Compatibility:* \> OpenCV 2.0
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*Author:* Edgar Riba
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Real time pose estimation of a textured object using ORB features, FlannBased matcher, PnP
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approach plus Ransac and Linear Kalman Filter to reject possible bad poses.
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- @subpage tutorial_interactive_calibration
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*Compatibility:* \> OpenCV 3.1
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*Author:* Vladislav Sovrasov
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Camera calibration by using either the chessboard, chAruco, asymmetrical circle or dual asymmetrical circle
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pattern. Calibration process is continious, so you can see results after each new pattern shot.
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As an output you get average reprojection error, intrinsic camera parameters, distortion coefficients and
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confidence intervals for all of evaluated variables.
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