@@ -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.
@@ -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.
@@ -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
@@ -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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