# A tour of mrcal: range dependence

## Previous

## The effect of range in differencing and uncertainty computations

Earlier I talked about model differencing and estimation of projection uncertainty. In both cases I glossed over one important detail that I would like to revisit now. A refresher:

- To compute a diff, I unproject \(\vec q_0\) to a point in space \(\vec p\) (in camera coordinates), transform it, and project that back to the other camera to get \(\vec q_1\)
- To compute an uncertainty, I unproject \(\vec q_0\) to (eventually) a point in space \(\vec p_\mathrm{fixed}\) (in some fixed coordinate system), then project it back, propagating all the uncertainties of all the quantities used to compute the transformations and projection.

The significant part is the specifics of "unproject \(\vec q_0\)". Unlike a
projection operation, an *unprojection* is ambiguous: given some
camera-coordinate-system point \(\vec p\) that projects to a pixel \(\vec q\), we
have \(\vec q = \mathrm{project}\left(k \vec v\right)\) *for all* \(k\). So an
unprojection gives you a direction, but no range. What that means in this case,
is that we must choose a range of interest when computing diffs or
uncertainties. It only makes sense to talk about a "diff when looking at points
\(r\) meters away" or "the projection uncertainty when looking out to \(r\) meters".

A surprising consequence of this is that while *projection* is invariant to
scaling (\(k \vec v\) projects to the same \(\vec q\) for any \(k\)), the uncertainty
of this projection is *not*:

Let's look at the projection uncertainty at the center of the imager at
different ranges for the `LENSMODEL_OPENCV8`

model we computed earlier:

mrcal-show-projection-uncertainty \ --vs-distance-at center \ --set 'yrange [0:0.1]' \ opencv8.cameramodel

So the uncertainty grows without bound as we approach the camera. As we move away, there's a sweet spot where we have maximum confidence. And as we move further out still, we approach some uncertainty asymptote at infinity. Qualitatively this is the figure I see 100% of the time, with the position of the minimum and of the asymptote varying.

Why is the uncertainty unbounded as we approach the camera? Because we're looking at the projection of a fixed point into a camera whose position is uncertain. As we get closer to the origin of the camera, the noise in the camera position dominates the projection, and the uncertainty shoots to infinity.

What controls the range where we see the lowest uncertainty? The range where we observed the chessboards. I will prove this conclusively in the next section. It makes sense: the lowest uncertainty corresponds to the region where we have the most information.

What controls the uncertainty at infinity? The empirical studies in the next section answer that conclusively.

This is a very important effect to characterize. In many applications the range of observations at calibration time varies significantly from the working range post-calibration. For instance, any application involving wide lenses will use closeup calibration images, but working images from further out. We don't want to compute a calibration where the calibration-time uncertainty is great, but the working-range uncertainty is poor.

I should emphasize that while unintuitive, this uncertainty-depends-on-range effect is very real. It isn't just something that you get out of some opaque equations, but it's observable in the field. Here're two real-world diffs of two calibrations computed from different observations made by the same camera a few minutes apart. Everything is the same, so I should be getting identical calibrations. A diff at infinity:

mrcal-show-projection-diff --unset key camera[01].cameramodel

And again at 0.5m (close to the range to the chessboards)

mrcal-show-projection-diff --distance 0.5 --unset key camera[01].cameramodel

Clearly the prediction that uncertainties are lowest at the chessboard range,
and increase as we move away to infinity is borne out here by just looking at
diffs, *without* computing the uncertainty curves. I didn't have to look very
hard to find calibrations that showed this. Any calibration from suboptimal
chessboard images (see next section) shows this effect.

## Next

We're now ready to find the best chessboard-dancing technique.