176 lines
7.4 KiB
Markdown
176 lines
7.4 KiB
Markdown
# Perspective-n-Point (PnP) pose computation {#calib3d_solvePnP}
|
|
|
|
## Pose computation overview
|
|
|
|
The pose computation problem @cite Marchand16 consists in solving for the rotation and translation that minimizes the reprojection error from 3D-2D point correspondences.
|
|
|
|
The `solvePnP` and related functions estimate the object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients, see the figure below (more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward and the Z-axis forward).
|
|
|
|
|
|
|
|
Points expressed in the world frame \f$ \bf{X}_w \f$ are projected into the image plane \f$ \left[ u, v \right] \f$
|
|
using the perspective projection model \f$ \Pi \f$ and the camera intrinsic parameters matrix \f$ \bf{A} \f$ (also denoted \f$ \bf{K} \f$ in the literature):
|
|
|
|
\f[
|
|
\begin{align*}
|
|
\begin{bmatrix}
|
|
u \\
|
|
v \\
|
|
1
|
|
\end{bmatrix} &=
|
|
\bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{T}_w
|
|
\begin{bmatrix}
|
|
X_{w} \\
|
|
Y_{w} \\
|
|
Z_{w} \\
|
|
1
|
|
\end{bmatrix} \\
|
|
\begin{bmatrix}
|
|
u \\
|
|
v \\
|
|
1
|
|
\end{bmatrix} &=
|
|
\begin{bmatrix}
|
|
f_x & 0 & c_x \\
|
|
0 & f_y & c_y \\
|
|
0 & 0 & 1
|
|
\end{bmatrix}
|
|
\begin{bmatrix}
|
|
1 & 0 & 0 & 0 \\
|
|
0 & 1 & 0 & 0 \\
|
|
0 & 0 & 1 & 0
|
|
\end{bmatrix}
|
|
\begin{bmatrix}
|
|
r_{11} & r_{12} & r_{13} & t_x \\
|
|
r_{21} & r_{22} & r_{23} & t_y \\
|
|
r_{31} & r_{32} & r_{33} & t_z \\
|
|
0 & 0 & 0 & 1
|
|
\end{bmatrix}
|
|
\begin{bmatrix}
|
|
X_{w} \\
|
|
Y_{w} \\
|
|
Z_{w} \\
|
|
1
|
|
\end{bmatrix}
|
|
\end{align*}
|
|
\f]
|
|
|
|
The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow transforming
|
|
a 3D point expressed in the world frame into the camera frame:
|
|
|
|
\f[
|
|
\begin{align*}
|
|
\begin{bmatrix}
|
|
X_c \\
|
|
Y_c \\
|
|
Z_c \\
|
|
1
|
|
\end{bmatrix} &=
|
|
\hspace{0.2em} ^{c}\bf{T}_w
|
|
\begin{bmatrix}
|
|
X_{w} \\
|
|
Y_{w} \\
|
|
Z_{w} \\
|
|
1
|
|
\end{bmatrix} \\
|
|
\begin{bmatrix}
|
|
X_c \\
|
|
Y_c \\
|
|
Z_c \\
|
|
1
|
|
\end{bmatrix} &=
|
|
\begin{bmatrix}
|
|
r_{11} & r_{12} & r_{13} & t_x \\
|
|
r_{21} & r_{22} & r_{23} & t_y \\
|
|
r_{31} & r_{32} & r_{33} & t_z \\
|
|
0 & 0 & 0 & 1
|
|
\end{bmatrix}
|
|
\begin{bmatrix}
|
|
X_{w} \\
|
|
Y_{w} \\
|
|
Z_{w} \\
|
|
1
|
|
\end{bmatrix}
|
|
\end{align*}
|
|
\f]
|
|
|
|
## Pose computation methods
|
|
@anchor calib3d_solvePnP_flags
|
|
|
|
Refer to the cv::SolvePnPMethod enum documentation for the list of possible values. Some details about each method are described below:
|
|
|
|
- cv::SOLVEPNP_ITERATIVE Iterative method is based on a Levenberg-Marquardt optimization. In
|
|
this case the function finds such a pose that minimizes reprojection error, that is the sum
|
|
of squared distances between the observed projections "imagePoints" and the projected (using
|
|
cv::projectPoints ) "objectPoints". Initial solution for non-planar "objectPoints" needs at least 6 points and uses the DLT algorithm.
|
|
Initial solution for planar "objectPoints" needs at least 4 points and uses pose from homography decomposition.
|
|
- cv::SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
|
|
"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete).
|
|
In this case the function requires exactly four object and image points.
|
|
- cv::SOLVEPNP_AP3P Method is based on the paper of T. Ke, S. Roumeliotis
|
|
"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17).
|
|
In this case the function requires exactly four object and image points.
|
|
- cv::SOLVEPNP_EPNP Method has been introduced by F. Moreno-Noguer, V. Lepetit and P. Fua in the
|
|
paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp).
|
|
- cv::SOLVEPNP_DLS **Broken implementation. Using this flag will fallback to EPnP.** \n
|
|
Method is based on the paper of J. Hesch and S. Roumeliotis.
|
|
"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct).
|
|
- cv::SOLVEPNP_UPNP **Broken implementation. Using this flag will fallback to EPnP.** \n
|
|
Method is based on the paper of A. Penate-Sanchez, J. Andrade-Cetto,
|
|
F. Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length
|
|
Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$
|
|
assuming that both have the same value. Then the cameraMatrix is updated with the estimated
|
|
focal length.
|
|
- cv::SOLVEPNP_IPPE Method is based on the paper of T. Collins and A. Bartoli.
|
|
"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method requires coplanar object points.
|
|
- cv::SOLVEPNP_IPPE_SQUARE Method is based on the paper of Toby Collins and Adrien Bartoli.
|
|
"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method is suitable for marker pose estimation.
|
|
It requires 4 coplanar object points defined in the following order:
|
|
- point 0: [-squareLength / 2, squareLength / 2, 0]
|
|
- point 1: [ squareLength / 2, squareLength / 2, 0]
|
|
- point 2: [ squareLength / 2, -squareLength / 2, 0]
|
|
- point 3: [-squareLength / 2, -squareLength / 2, 0]
|
|
- cv::SOLVEPNP_SQPNP Method is based on the paper "A Consistently Fast and Globally Optimal Solution to the
|
|
Perspective-n-Point Problem" by G. Terzakis and M.Lourakis (@cite Terzakis2020SQPnP). It requires 3 or more points.
|
|
|
|
## P3P
|
|
|
|
The cv::solveP3P() computes an object pose from exactly 3 3D-2D point correspondences. A P3P problem has up to 4 solutions.
|
|
|
|
@note The solutions are sorted by reprojection errors (lowest to highest).
|
|
|
|
## PnP
|
|
|
|
The cv::solvePnP() returns the rotation and the translation vectors that transform a 3D point expressed in the object
|
|
coordinate frame to the camera coordinate frame, using different methods:
|
|
- P3P methods (cv::SOLVEPNP_P3P, cv::SOLVEPNP_AP3P): need 4 input points to return a unique solution.
|
|
- cv::SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
|
|
- cv::SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation.
|
|
Number of input points must be 4. Object points must be defined in the following order:
|
|
- point 0: [-squareLength / 2, squareLength / 2, 0]
|
|
- point 1: [ squareLength / 2, squareLength / 2, 0]
|
|
- point 2: [ squareLength / 2, -squareLength / 2, 0]
|
|
- point 3: [-squareLength / 2, -squareLength / 2, 0]
|
|
- for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
|
|
|
|
## Generic PnP
|
|
|
|
The cv::solvePnPGeneric() allows retrieving all the possible solutions.
|
|
|
|
Currently, only cv::SOLVEPNP_P3P, cv::SOLVEPNP_AP3P, cv::SOLVEPNP_IPPE, cv::SOLVEPNP_IPPE_SQUARE, cv::SOLVEPNP_SQPNP can return multiple solutions.
|
|
|
|
## RANSAC PnP
|
|
|
|
The cv::solvePnPRansac() computes the object pose wrt. the camera frame using a RANSAC scheme to deal with outliers.
|
|
|
|
More information can be found in @cite Zuliani2014RANSACFD
|
|
|
|
## Pose refinement
|
|
|
|
Pose refinement consists in estimating the rotation and translation that minimizes the reprojection error using a non-linear minimization method and starting from an initial estimate of the solution. OpenCV proposes cv::solvePnPRefineLM() and cv::solvePnPRefineVVS() for this problem.
|
|
|
|
cv::solvePnPRefineLM() uses a non-linear Levenberg-Marquardt minimization scheme @cite Madsen04 @cite Eade13 and the current implementation computes the rotation update as a perturbation and not on SO(3).
|
|
|
|
cv::solvePnPRefineVVS() uses a Gauss-Newton non-linear minimization scheme @cite Marchand16 and with an update of the rotation part computed using the exponential map.
|
|
|
|
@note at least three 3D-2D point correspondences are necessary.
|