Guillaume Lavoué Homepage

Softwares


MEPP and MEPP2 mesh processing platforms


The Mesh Processing Plateform (MEPP) of our research team is publicly available on GitHub. It is an open source plateform based on CGAL, Qt and libQGLViewer which supports Windows, Linux and Mac OS X. This plateform allows the processing of static and dynamic 3D meshes and contains differents tools from the team (curvature calculation, segmentation, MSDM and MSDM2 perceptual metrics, fast boolean operations, Joint compression/watermarking, progressive compression).

A new version, MEPP2, has been released in 2018, it is available on GitHub. It supports several data-structures (e.g., CGAL Surface_Mesh, CGAL Polyhedron, OpenMesh). It includes MSDM2, and progressive compression.

These platforms include the source code of the following papers:

Lavoué, G., Drelie Gelasca, E., Dupont, F., Baskurt, A., Ebrahimi, T., Perceptually driven 3D distance metrics with application to watermarking, SPIE Applications of Digital Image Processing XXIX, San Diego, August 2006.

Guillaume Lavoué, A Multiscale Metric for 3D Mesh Visual Quality Assessment, Computer Graphics Forum (Proceedings of Eurographics Symposium on Geometry Processing 2011), vol. 30, No. 5, pp. 1427-1437, 2011.

Ho Lee, Cagatay Dikici, Guillaume Lavoué and Florent Dupont, Joint Reversible Watermarking and Progressive Compression of 3D Meshes, The Visual Computer (35 best papers from Computer Graphics International 2011), vol. 27, No. 6-8, pp. 781-792, 2011.

Ho Lee, Guillaume Lavoué and Florent Dupont, Rate-distortion optimization for progressive compression of 3D mesh with color attributes,The Visual Computer, vol. 28, No. 2, pp. 137-153, 2012.

You can also find below some other MS-Windows demonstration softwares about some algorithms from me and my co-workers.
If you find any bugs or need some more explanations, please contact glavoue@liris.cnrs.fr.

  
MSDM : A perceptual distance measure between 3D meshes

This measure is asymetric and provides a score which reflects the perceptual distance between two 3D objects; Its value tends toward 1 (theoretical limit) when the measured objects are visually very different and is equal to 0 for identical ones.

This metric is based on curvature tensors which are integrated over geodesic regions. The radius of these regions influences the results of the metric. Indeed, for a large radius, the metric is less sensitive to small details (it corresponds to the case where the observer is far from the objects for instance). We recommend 0.005 for a rather high sensitivity and 0.01 for a lower sensitivity.

Warning: this metric works only for two meshes sharing the same connectivity and same vertex order in the mesh files.

The MS-Windows executable is available here (last release 8/09/2009) and the source code is available here (8/09/2009).

The latest release (15/11/2010)  of MSDM  is available in the MEPP and MEPP2 platforms.

A very nice MEX implementation of MSDM (interface with Matlab) has been provided by Xavier Rolland-Nevière (with a cleaning of the source code). It is available here.

References: 

Lavoué, G., Drelie Gelasca, E., Dupont, F., Baskurt, A., Ebrahimi, T., Perceptually driven 3D distance metrics with application to watermarking, SPIE Applications of Digital Image Processing XXIX, San Diego, August 2006.

Guillaume Lavoué and Massimiliano Corsini, A comparison of perceptually-based metrics for objective evaluation of geometry processing, IEEE Transactions on Multimedia, Vol. 12, No. 7, pp. 636-649, 2010.

3D mesh roughness calculation

This measure provides the roughness value for each vertex of a given 3D mesh, as a local measure of geometric noise. This estimator depends on a scale parameter epsilon which determines the size (i.e. the frequency) of the details that have to be considered as noise and that can lead to a masking effect.

The MS-Windows executable is available here (last release 4/09/2008).

A clean easy-to-compile source code is available here (provided by Arnaud Delmotte, February 2018).

Reference: Lavoué, G., A Local Roughness Measure for 3D Meshes and its Application to Visual Masking, ACM Transactions on Applied Perception, Vol. 5, No. 4, Article 21, 2009.

Mesh clustering based on Markov Random Field

This algorithm provides a clustering / labeling of a 3D mesh given any field of scalar values associated with its vertices. It is based on Markov Random Fields and allows to integrate both the attributes and the geometry in the clustering, while providing an optimal global solution.

It takes as inputs a mesh file (XXX.obj or XXX.off) and an attribute file (XXX_attribute.att) and the output is a Label file (XXX__labels.lb).

The MS-Windows executable is available here (last release 4/09/2008).

Reference: Lavoué, G. and Wolf, C., Markov Random Fields for Improving 3D Mesh Analysis and Segmentation, Eurographics 2008 Workshop on 3D Object Retrieval, pp. 25-32, Crete, Greece, April 2008.

   

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THIS WORK IS PROVIDED "AS IS". THE CREATORS HEREBY DISCLAIM ALL WARRANTIES RELATING TO THIS SOFTWARE AND ITS DOCUMENTATION FILE, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO DAMAGE TO HARDWARE, SOFTWARE AND/OR DATA FROM USE OF THIS WORK. IN NO EVENT WILL THE CREATORS OF THIS PRODUCT BE LIABLE TO YOU OR ANY OTHER PARTY FOR ANY DAMAGES.