This is an implementation of JRMPC on aligning point clouds in consecutive pairs, and compute the transformation matrix relative to initial guess (identity) refering to Kitti format. 
See kitti.m for detail. The alignment result can be visualized using the alignPlot function. 

Note that JRMPC is an algorithm aiming at jointly aligning sequences, not in pairwise. Just for test use.

Reference to original file:
This software contains the matlab code of the JRMPC algorithm, which is
presented in the paper 

G.Evangelidis, D. Kounades-Bastian, R. Horaud, E. Psarakis, "A Generative 
Model for the Joint Registration of Multiple Point Sets", ECCV, 2014

Two demos are also provided that use the above algorithm to jointly register multiple views.

demoJrmpSynthetic reads the views (point clouds) from ./sytheticData/ then
calls JRMPC algorithm to do the registration and finally visualizes the final alignment
and also measures convergence in terms of the ground rotation. 
./sytheticData/ contains partial views and 360-views of Stanford 3D model 
"Bunny", after adding noise and outliers. Note also that each set is downsampled 
by its own factor. The case of partial views is similar to what has been used in 
the paper. 

Filenames of partial views: cutView1.txt, cutView2.txt ...
Filenames of 360-views: View1.txt View2.txt ...

demoJrmpcTof reads the point sets (views) from ./tofData/, calls JRMPC to do the registration
and then visualizes the final alignment. ./tofData/ contains data acquired using a TOF camera,
the files have also color information in the last 3 columns, acquired using a stereo pair of
still cameras calibrated with the TOF. Note that color information is not used for registration, 
while it is not very accurate close to occlusions. We suggest using color information
only for visualization.


The function removePointsAndFlatCenters is a helper function used by the above demos
It takes the output of JRMPC and tries to "detect" outliers in X and V, as described in the
paper.


For documentation on removePointsAndFlatCenters type in the matlab console:

    >> help removePointsAndFlatCenters

For documentation on jrmpc, type in matlab console:

    >> jrmpc

or simply call it with no arguments:

    >> jrmpc()


Note that:

1) Sometimes, we initialize the cluster centers with random points (points on a sphere surface) and we scale 
them to have the same order with data points. However, you may want to initialise the cluster centers
by subsampling one of the point sets (preferably the ‘middle’ one), or by subsampling all the registered point-sets 
if they are already roughly registered. 

2) If you have a good initialization for the point centers, you may want to keep it fixed for some iterations
in the beginning (just comment out the lines that update variable X) until variances converge to the correct order,
and then let both be updated. 

3) You may want to tune the parameters based on your datasets and/or include any prior knowledge
through your initialisation. If do not have any prior knowledge you can play with the order of variance ’S’ 
(.1, 1, 10,100,..) and/or the value of ‘gamma’ (try also zero value for gamma). 
You may also want to subsample your data to reduce the complexity. Most likely, a reasonable subsampling does 
not affect the accuracy of rotations/translations.

4) The visualisation of the registered sets in case of TOF data is very difficult and slow in Matlab. 
We strongly recommend to use the provided function mat2off.m and save the registered point set to OFF files.
Then use an compatible software to display the OFF file.


For any bugs, questions and/or comments, please contact:

Georgios Evangelidis, [email protected]
Dionyssos Kounades-Bastian, [email protected]


last update:
24/05/2015