Introduction
This page explains how to use the 3points version. For the general npts version, see matlab/demo.m
in GitHub.
How to use the Matlab version with sample data
Usage
After unzipping the downloaded file, you will have the following files in a single directory named tnm
.
data/input{1,2,3}.png
: Three input images. They are the original pictures WITH lens distortions.data/input{1,2,3}.txt
: Three input data points extracted frominput{1,2,3}.png
. The points are taken from undistorted images.camera.txt
: The intrinsic parameter of the camera.model.txt
: The reference object.demo.m
: A demo program to run our method.tnm.m
: An implementation of our calibration method.sub_p3p.m
: A subfunction called fromtnm.m
to solve P3P problem.sub_tnm_orth.m
: A subfunction called fromtnm.m
.sub_tnm_rt.m
: A subfunction called fromtnm.m
.sub_reproj.m
: To calc reprojection error.
Start Matlab, and change the working directory to the tnm
directory. Run demo.m
and you should see the following outputs and a popup window that visualizes the results.
> demo.m
Average reprojection error by TNM : 0.353531 pixel.
==== Parameters by TNM ====
R =
0.9552 0.0316 0.2942
0.0262 0.9994 0.0221
0.2947 0.0134 0.9555
T =
71.6716
84.3404
120.2696
n1 =
0.2679
0.0307
0.9629
n2 =
0.4356
0.0844
0.8962
n3 =
0.0443
0.0112
0.9990
d1 =
386.2302
d2 =
355.0478
d3 =
404.7066
Brief descriptions of the code
tnm.m
: toplevel driver
The figure below illustrates the measurement model of our method.
In this figure, we use the following notations. Notice that $${}^Yx$$ denotes $$x$$ in $$Y$$ coordinate system.
 $$C$$ : camera.
 $$\pi_j (j=1,2,3)$$ : three mirrors.
 $${}^Cn_j (j=1,2,3)$$ : the normal vector of $$\pi_j$$.
 $$d_j (j=1,2,3)$$ : the distance from $$C$$ to $$\pi_j$$.
 $${}^Xp^i (i=1,2,3)$$ : three reference points in the reference object coordinate system.
 $${}^Cp^i (i=1,2,3)$$ : $${}^Xp^i$$ in the camera $$C$$ coordinate system.
 $${}^Cp^i_j (i,j=1,2,3)$$ : Reflection of $${}^Xp^i$$ by $$\pi_j$$ in the camera $$C$$ coordinate system.
 $$q^i_j (i,j=1,2,3)$$ : 2D projection of $${}^Cp^i_j$$ in the camera $$C$$ image screen.
The goal is to estimate the relative rotation $$R$$ and translation $$T$$ between the camera $$C$$ and the reference $$X$$ which satisfy
$${}^Cp^i = R \cdot {}^Xp^i + T (i=1,2,3) $$. (1)
by knowing $$q^i_j (i,j=1,2,3)$$, the projections of $${}^Xp^i$$ observed via three different mirrors $$\pi_j (j=1,2,3)$$ of unknown positions. The orientation and the position of t
he mirrors (the distances from the camera to the mirrors) are also estimated as a result.
So the input / output of the abovementioned tnm.m
can be expressed as follows.
tnm.m
 Input $$q^i_j (i,j=1,2,3)$$ : 2D projections of three reference points observed via three mirrors $$\pi_j (j=1,2,3)$$.
 Output:
 $$R, T$$ : The relative posture and position between the camera $$C$$ and the reference $$X$$.
 $${}^Cn_j, d_j (j=1,2,3)$$ : The orientations and distances of the three mirrors $$\pi_j (j=1,2,3)$$.
Subfunctions
Our mirrorbased calibration method consists of the following three steps, and we provide implementations corresponding to them.

sub_p3p.m
: P3P per mirrored image. Input:
 $${}^Xp^i (i=1,2,3)$$ : Three reference points, and
 $$q^i (i)$$ : their projections observed via a mirror $$\pi$$.
 Output: Up to 4 possible solutions of $${}^Cp^i$$
 Input:

sub_tnm_orth.m
: Unique solution selection using an orthogonality constraint. Input: 64 sets of $${}^Cp^i_j (i,j=1,2,3)$$.
 Output: The set of $${}^Cp^i_j (i,j=1,2,3)$$ which follows the orthogonality constraint best.
sub_tnm_rt.m
: Linear estimation of $$R$$ and $$T$$. Input: $${}^Cp^i_j (i,j=1,2,3)$$.
 Output: $$R, T, n_j, d_j (j=1,2,3)$$.
And in addition, we use the following subfunction for evaluation purpose.
sub_reproj.m
: Reprojection error evaluation Input:
 $${}^Xp^i (i=1,\dots)$$ : Any number of reference points.
 $$q^i_j (i=1,\dots, j=1,2,3)$$ : The projections of the reference points via mirrors.
 $$R, T, n_j, d_j (j=1,2,3)$$ : Estimated parameters.
 Output: Average reprojection error.
 Input:
How to use the OpenCV version with sample data
Usage
After unzipping the downloaded file, you will have the following files in a single directory named ./tnmopencv
.
data/input{1,2,3}.png
: Three input images. They are the original pictures WITH lens distortions.data/input{1,2,3}.txt
: Three input data points extracted from input{1,2,3}.png. The points are taken from undistorted images.Makefile
: makefile.demo.cc
: A demo program to run our method.sub_solveP3P.h
: A subfunction called from tnm.h to solve P3P problem.tnm.h
: An implementation of our calibration method.
This code requires OpenCV 2.3. We used libcvdev package in Debian wheezy. For Debian/Ubuntu, try
$ sudo aptget f install libcvdev libcvauxdev libhighguidev g++ make
to install requisite libraries and compilation tools, and then exec
$ make
to compile the code.
For Visual C++ on Windows, please simply import demo.cc
, sub_solveP3P.h
, and tnm.h
into a new project, and compile it (you need to setup additional include path and libraries for Op
enCV, of course).
Once compiled, run the binary (named demo
) with no args in the ./tnmopencv
directory.
$ ./demo
loading 'data/input1.txt' = [263.854279, 284.595978;
380.608337, 284.673645;
261.375946, 355.315582]
loading 'data/input2.txt' = [187.462204, 264.845764;
302.664276, 261.313538;
183.416656, 338.876984]
loading 'data/input3.txt' = [393.80719, 301.828278;
529.568542, 311.569794;
391.23999, 374.259766]
loading 'data/model.txt' = [0, 0, 0;
175, 0, 0;
0, 100, 0]
loading 'data/camera.txt' = [487.910797, 0, 324.31308;
0, 487.558441, 237.003937;
0, 0, 1]
Average reprojection error by TNM : 0.353531 pixels
==== Parameters by TNM ====
R = [0.9552278293542109, 0.0315692738655991, 0.2941822138995512;
0.02622804982091903, 0.9994120031779081, 0.02208477544627536;
0.294706436016986, 0.01338016634873504, 0.9554941589139341]
T = [71.67155976406129; 84.34036742140127; 120.2696306131992]
n1 = [0.2679446927390354; 0.03066392138399924; 0.9629461903753189]
n2 = [0.4356434955333516; 0.08437398428883026; 0.8961561111629551]
n3 = [0.04427539436835728; 0.01119106464381498; 0.9989566805050478]
d1 = 386.23
d2 = 355.048
d3 = 404.707
This program automatically loads data from data/input{1,2,3}.txt
, and then outputs the estimated parameters to stdout.
Brief descriptions of the code
The structure of the code is very straightforward. Please visit the main()
function in demo.cc
first. The flow of main()
is:
 load data from files (
model.txt
,input{1,2,3}.txt
) byload()
function defined indemo.cc
.  run calibration by
tnm()
function defined in tnm.h. What this function does inside is:  call
sub_solveP3P()
defined insub_solveP3P.h
for each input data (= the Matlab function intnmmatlab/sub_p3p.m
),  call
sub_tnm_orth()
defined intnm.h
(= the Matlab function intnmmatlab/sub_tnm_orth.m
), and  call
sub_tnm_rt()
defined intnm.h
(= the Matlab function intnmmatlab/sub_tnm_rt.m
).  run
sub_reproj()
defined indemo.cc
to evaluate the reprojection error (= the Matlab function intnmmatlab/sub_reproj.m
).
So to reuse the code for your own project, copy tnm.h
and sub_solveP3P.h
, and then use tnm()
for calibration. To understand how to prepare the data, please consult the load()
f
unction in demo.cc
.
How to use the code with your own data
To calibrate your own system, please follow the process below. In short, update data/model.txt
and data/input{1,2,3}.txt
, then run the program again.
 Suppose you have a reference object $$X$$ and a camera $$C$$.
 The intrinsic parameters of $$C$$ should be provided. You can use OpenCV to estimate them.
 Capture three images of $$X$$ as $$I_1, I_2, I_3$$ via mirrors $$\pi_1, \pi_2, \pi_3$$ under different poses.
 Detect three points of $$X$$ for each of the images ($$I_1, I_2, I_3$$).
 Here we assume that the images are rectified (undistorted) using the intrinsic parameters before the detection.
 If you used a chessboard pattern as $$X$$ and used OpenCV findChessboardCorners() to detect it in $$I_1, I_2, I_3$$ automatically, you need to flip the detection result because the detector does not account for the observation via mirror.

Store the data into
data/model.txt
anddata/input{1,2,3}.txt
.data/model.txt
is a lineoriented plain text file each of lines represents the 3D position of a reference point in $$X$$.0.000000 0.000000 0.000000 175.000000 0.000000 0.000000 0.000000 100.000000 0.000000

data/input{1,2,3}.txt
are also lineoriented plain text files, and each of lines represents the 2D projection of the corresponding 3D reference point indata/model.txt
. For examp le, the first line below is the projection of the first reference point $$(0,0,0)$$ defined in the first line ofdata/model.txt
.380.608337 284.673645 263.854279 284.595978 377.368225 350.688141

Exec the demo program again, and you will get the result.