mrcal lens models

This is the basic "pinhole" model with 4 parameters: the core. Projection of a point \(\vec p\) is defined as

\[\vec q = \left[ \begin{aligned} f_x \frac{p_x}{p_z} + c_x \\ f_y \frac{p_y}{p_z} + c_y \end{aligned} \right] \]

This model is defined only in front of the camera, and projects to infinity as we approach 90 degrees off the optical axis (\(p_z \rightarrow 0\)). Straight lines in space remain straight under this projection, and observations of the same plane by two pinhole cameras define a homography. This model can be used for stereo rectification, although it only works well with long lenses. Longer lenses tend to have roughly pinhole behavior, but no real-world lens follows this projection, so this exists for data processing only.

This is another trivial model that exists for data processing, and not to represent real lenses. Like the pinhole model, this has just the 4 core parameters.

To define the projection of a point \(\vec p\), let's define the angle off the optical axis:

\[ \theta \equiv \tan^{-1} \frac{\left| \vec p_{xy} \right|}{p_z} \]

then

\[ \vec u \equiv \frac{\vec p_{xy}}{\left| \vec p_{xy} \right|} 2 \tan\frac{\theta}{2} \]

and

\[\vec q = \left[ \begin{aligned} f_x u_x + c_x \\ f_y u_y + c_y \end{aligned} \right] \]

This model is able to project behind the camera, and has a single singularity: directly opposite the optical axis. mrcal refers to \(\vec u\) as the normalized stereographic projection; we get the projection \(\vec q = \vec u\) when \(f_x = f_y = 1\) and \(c_x = c_y = 0\)

Note that the pinhole model can be defined in the same way, except the pinhole model has \(\vec u \equiv \frac{\vec p_{xy}} {\left| \vec p_{xy} \right|} \tan \theta\). And we can thus see that for long lenses the pinhole model and the stereographic model function similarly: \(\tan \theta \approx 2 \tan \frac{\theta}{2}\) as \(\theta \rightarrow 0\)

This is a standard equirectangular projection. It's a trivial model useful not for representing lenses, but for describing the projection function of wide panoramic images. This works just like latitude an longitude on a globe, with a linear angular map on latitude and longitude. The 4 intrinsics core parameters are used to linearly map latitude, longitude to pixel coordinates. The full projection expression to map a camera-coordinate point \(\vec p\) to an image pixel \(\vec q\):

\[ \vec q = \left[ \begin{aligned} f_x \, \mathrm{lon} + c_x \\ f_y \, \mathrm{lat} + c_y \end{aligned} \right] = \left[ \begin{aligned} f_x \tan^{-1}\left(\frac{p_x}{p_z}\right) + c_x \\ f_y \sin^{-1}\left(\frac{p_y}{\left|\vec p\right|}\right) + c_y \end{aligned} \right] \]

So \(f_x\) and \(f_y\) specify the angular resolution, in pixels/radian.

For normal lens models the optical axis is at \(\vec p = \left[ \begin{aligned} 0 \\ 0 \\ 1 \end{aligned} \right]\), and projects to roughly the center of the image, roughly at \(\vec q = \left[ \begin{aligned} c_x \\ c_y \end{aligned} \right]\). This model has \(\mathrm{lon} = \mathrm{lat} = 0\) at the optical axis, which produces the same, usual \(\vec q\). However, this projection doesn't represent a lens and there is no "camera" or an "optical axis". The view may be centered anywhere, so \(c_x\) and \(c_y\) could be anything, even negative.

The special case of \(f_x = f_y = 1\) and \(c_x = c_y = 0\) (the default values in mrcal.project_lonlat()) produces a normalized equirectangular projection:

\[ \vec q_\mathrm{normalized} = \left[ \begin{aligned} \mathrm{lon} \\\mathrm{lat} \end{aligned} \right] \]

This projection has a singularity at the poles, approached as \(x \rightarrow 0\) and \(z \rightarrow 0\).

This is a "transverse equirectangular projection". It works just like LENSMODEL_LONLAT, but rotated 90 degrees. So instead of a globe oriented as usual with a vertical North-South axis, this projection has a horizontal North-South axis. The projected \(x\) coordinate corresponds to the latitude, and the projected \(y\) coordinate corresponds to the longitude.

As with LENSMODEL_LONLAT, lenses do not follow this model. It is useful as the core of a rectified view used in stereo processing. The full projection expression to map a camera-coordinate point \(\vec p\) to an image pixel \(\vec q\):

\[ \vec q = \left[ \begin{aligned} f_x \, \mathrm{lat} + c_x \\ f_y \, \mathrm{lon} + c_y \end{aligned} \right] = \left[ \begin{aligned} f_x \sin^{-1}\left(\frac{p_x}{\left|\vec p\right|}\right) + c_x \\ f_y \tan^{-1}\left(\frac{p_y}{p_z}\right) + c_y \end{aligned} \right] \]

As with LENSMODEL_LONLAT, \(f_x\) and \(f_y\) specify the angular resolution, in pixels/radian. And \(c_x\) and \(c_y\) specify the projection at the optical axis \(\vec p = \left[ \begin{aligned} 0 \\ 0 \\ 1 \end{aligned} \right]\).

The special case of \(f_x = f_y = 1\) and \(c_x = c_y = 0\) (the default values in mrcal.project_latlon()) produces a normalized transverse equirectangular projection:

\[ \vec q_\mathrm{normalized} = \left[ \begin{aligned} \mathrm{lat} \\\mathrm{lon} \end{aligned} \right] \]

This projection has a singularity at the poles, approached as \(y \rightarrow 0\) and \(z \rightarrow 0\).

These are simple parametric models that have the given number of "distortion" parameters in addition to the 4 core parameters. The projection behavior is described in the OpenCV documentation. These do a reasonable job in representing real-world lenses, and they're compatible with many other tools. The projection function is

\begin{align*} \vec P &\equiv \frac{\vec p_{xy}}{p_z} \\ r &\equiv \left|\vec P\right| \\ \vec P_\mathrm{radial} &\equiv \frac{ 1 + k_0 r^2 + k_1 r^4 + k_4 r^6}{ 1 + k_5 r^2 + k_6 r^4 + k_7 r^6} \vec P \\ \vec P_\mathrm{tangential} &\equiv \left[ \begin{aligned} 2 k_2 P_0 P_1 &+ k_3 \left(r^2 + 2 P_0^2 \right) \\ 2 k_3 P_0 P_1 &+ k_2 \left(r^2 + 2 P_1^2 \right) \end{aligned}\right] \\ \vec P_\mathrm{thinprism} &\equiv \left[ \begin{aligned} k_8 r^2 + k_9 r^4 \\ k_{10} r^2 + k_{11} r^4 \end{aligned}\right] \\ \vec q &= \vec f_{xy} \left( \vec P_\mathrm{radial} + \vec P_\mathrm{tangential} + \vec P_\mathrm{thinprism} \right) + \vec c_{xy} \end{align*}

The parameters are \(k_i\). For any N-parameter OpenCV model the higher-order terms \(k_i\) for \(i \geq N\) are all 0. So the tangential distortion terms exist for all the models, but the thin-prism terms exist only for LENSMODEL_OPENCV12. The radial distortion is a polynomial in LENSMODEL_OPENCV4 and LENSMODEL_OPENCV5, but a rational for the higher-order models. Practically-speaking LENSMODEL_OPENCV8 works decently well for wide lenses. For non-fisheye lenses, LENSMODEL_OPENCV4 and LENSMODEL_OPENCV5 work ok. I'm sure scenarios where LENSMODEL_OPENCV12 is beneficial exist, but I haven't come across them.

mrcal supports LENSMODEL_CAHVOR, a lens model used in a number of tools at JPL. The LENSMODEL_CAHVOR model has 5 "distortion" parameters in addition to the 4 core parameters. This support exists only for compatibility, and there's no reason to use it otherwise. This model is described in:

Gennery, D.B. 2001. Least-squares camera calibration including lens distortion and automatic editing of calibration points. In Calibration and Orientation of Cameras in Computer Vision, A. Gruen and T.S. Huang (Eds.), Springer-Verlag, pp. 123–136

This is an extended flavor of LENSMODEL_CAHVOR to support wider lenses. The LENSMODEL_CAHVORE model has 8 "distortion" parameters in addition to the 4 core parameters. As with LENSMODEL_CAHVOR this support exists only for compatibility, and there's no reason to use it otherwise.

CAHVORE is a noncentral projection, so more infrastructure is required to make unproject() do something reasonable in all cases. Thus projection uncertainty and differencing don't work for this model, and throw an error.

LENSMODEL_CAHVORE models have a single configuration parameter: linearity.

• linearity = 0 is a "fisheye" CAHVORE model. This is referred to as CAHVORE2 in JPL tools
• linearity = 1 is a "perspective" CAHVORE model. This is referred to as CAHVORE1 in JPL tools
• Any other linearity is a "general" CAHVORE model. This is referred to as CAHVORE3 in JPL tools

Some notes:

• The last 3 intrinsic parameters of LENSMODEL_CAHVORE are the \(\vec E\) vector. If these are 0, this becomes a central projection, and mrcal.unproject() is able to work normally. If you want to compute differences you need mrcal.unproject(), so you must "centralize" the model by setting intrinsics[-3:] = 0. This ignores any noncentral effects, which could be significant close to the lens. Doing this also invalidates the uncertainty logic. More general noncentral projection logic will be added in a future release of mrcal, and this situation may improve then
• A LENSMODEL_CAHVORE projection with \(\vec E = 0\) and linearity = 1 is equivalent to a LENSMODEL_CAHVOR projection

This model is described in:

Gennery, D.B. Generalized Camera Calibration Including Fish-Eye Lenses. Int J Comput Vision 68, 239–266 (2006)

This is a stereographic model with correction factors. It is mrcal's attempt to model real-world lens behavior with more fidelity than the usual parametric models make possible.

To compute a projection using this model, we first compute the normalized stereographic projection \(\vec u\) as in the LENSMODEL_STEREOGRAPHIC definition above:

\[ \theta \equiv \tan^{-1} \frac{\left| \vec p_{xy} \right|}{p_z} \]

\[ \vec u \equiv \frac{\vec p_{xy}}{\left| \vec p_{xy} \right|} 2 \tan\frac{\theta}{2} \]

Then we use \(\vec u\) to look-up a \(\Delta \vec u\) using two separate splined surfaces:

\[ \Delta \vec u \equiv \left[ \begin{aligned} \Delta u_x \left( \vec u \right) \\ \Delta u_y \left( \vec u \right) \end{aligned} \right] \]

and we then define the rest of the projection function:

\[\vec q = \left[ \begin{aligned} f_x \left( u_x + \Delta u_x \right) + c_x \\ f_y \left( u_y + \Delta u_y \right) + c_y \end{aligned} \right] \]

The \(\Delta \vec u\) are the off-stereographic terms. If \(\Delta \vec u = 0\), we get a plain stereographic projection.

Much more detail about this model is available on the splined models page.