JRMPC_v0.9.2.zip_JRMPC_The Point

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) 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). 2) You may want to tune the parameters based on your datasets and/or include any prior knowledge through your initialisation. You may also want to subsample your data to reduce the complexity. Most likely, a reasonable subsampling does not affect the accuracy of roto-translations. 3) 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 and/or comments, please contact: Georgios Evangelidis, georgios.evangelidis@inria.fr Dionyssos Kounades-Bastian, dionyssos.kounades-bastian@inria.fr last update: 18/12/2014