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@ -450,8 +450,10 @@ enum SolvePnPMethod { |
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SOLVEPNP_ITERATIVE = 0, |
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SOLVEPNP_EPNP = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp
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SOLVEPNP_P3P = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete
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SOLVEPNP_DLS = 3, //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct
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SOLVEPNP_UPNP = 4, //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive
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SOLVEPNP_DLS = 3, //!< **Broken implementation. Using this flag will fallback to EPnP.** \n
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//!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct
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SOLVEPNP_UPNP = 4, //!< **Broken implementation. Using this flag will fallback to EPnP.** \n
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//!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive
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SOLVEPNP_AP3P = 5, //!< An Efficient Algebraic Solution to the Perspective-Three-Point Problem @cite Ke17
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SOLVEPNP_IPPE = 6, //!< Infinitesimal Plane-Based Pose Estimation @cite Collins14 \n
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//!< Object points must be coplanar.
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@ -808,7 +810,7 @@ vectors, respectively, and further optimizes them. |
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- **SOLVEPNP_ITERATIVE** Iterative method is based on a Levenberg-Marquardt optimization. In |
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this case the function finds such a pose that minimizes reprojection error, that is the sum |
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of squared distances between the observed projections imagePoints and the projected (using |
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projectPoints ) objectPoints . |
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@ref projectPoints ) objectPoints . |
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- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang |
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"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). |
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In this case the function requires exactly four object and image points. |
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@ -817,9 +819,11 @@ In this case the function requires exactly four object and image points. |
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In this case the function requires exactly four object and image points. |
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- **SOLVEPNP_EPNP** Method has been introduced by F. Moreno-Noguer, V. Lepetit and P. Fua in the |
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paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp). |
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- **SOLVEPNP_DLS** Method is based on the paper of J. Hesch and S. Roumeliotis. |
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- **SOLVEPNP_DLS** **Broken implementation. Using this flag will fallback to EPnP.** \n |
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Method is based on the paper of J. Hesch and S. Roumeliotis. |
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"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct). |
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- **SOLVEPNP_UPNP** Method is based on the paper of A. Penate-Sanchez, J. Andrade-Cetto, |
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- **SOLVEPNP_UPNP** **Broken implementation. Using this flag will fallback to EPnP.** \n |
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Method is based on the paper of A. Penate-Sanchez, J. Andrade-Cetto, |
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F. Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length |
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Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ |
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assuming that both have the same value. Then the cameraMatrix is updated with the estimated |
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@ -1143,9 +1147,11 @@ In this case the function requires exactly four object and image points. |
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In this case the function requires exactly four object and image points. |
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- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the |
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paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp). |
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- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. |
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- **SOLVEPNP_DLS** **Broken implementation. Using this flag will fallback to EPnP.** \n |
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Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. |
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"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct). |
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- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, |
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- **SOLVEPNP_UPNP** **Broken implementation. Using this flag will fallback to EPnP.** \n |
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Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, |
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F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length |
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Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ |
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assuming that both have the same value. Then the cameraMatrix is updated with the estimated |
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