Current Research

sensaas

Molecular similarity based on 3D point set registration

In the context of the SENSAAS Project (Project initially funded by « Académie 4 - Complexité et diversité du vivant », UCA, 2019-2020), we develop alternative methods for detecting similar molecules, by comparing associate 3D shapes.

One Related Publication:
sensaas: Shape‐based Alignment by Registration of Colored Point‐based Surfaces has been accepted in Molecular Informatics (Wiley). Authors: Dominique DOUGUET, Frédéric PAYAN. Full Paper (Open Access)

lidar

Processing of gigantic point clouds

In the context of the PhD of Arnaud Bletterer (2014-2018) [Manuscript (in french)] we proposed a local graph-based structure to deal with sets of LiDAR acquisitions. We showed how this structure is particularly suitable for processing gigantic points clouds, for resampling for instance.

One Related Publication:
A. Bletterer, F. Payan and M. Antonini, "A local graph-based structure for processing gigantic aggregated 3D point clouds," in IEEE Transactions on Visualization and Computer Graphics, 2020, december. DOI: 10.1109/TVCG.2020.3042588

hs

Progressive Decomposition of meshes for Geosciences

In collaboration with IFP Energies Nouvelles, we proposed an efficient progressive decomposition for hexahedral meshes coming from geosciences. Our scheme generates a hierarchy of meshes at increasing levels of resolution, while ensuring a geometrical coherency over the resolutions. Our main contribution is a lossless and reversible wavelet filtering that takes into account the geometrical discontinuities but also manages the categorial properties associated to the cells.

One Related Publication:

Jean-Luc Peyrot, Laurent Duval, Frédéric PAYAN, Lauriane Bouard, Lenaic Chizat, Sébastien Schneider, Marc Antonini, HexaShrink, an exact progressive framework for hexahedral meshes representation with attributes and discontinuities: multiresolution rendering and storage of geoscience models, in Computational Geosciences (Springer), March, 2019.

Older Research

sampling

Surface Sampling and Semi-Regular Reconstruction

During Jean-Luc Peyrot's PhD (2011-2014), I worked on the resampling of surface meshes, and then on the semi-regular reconstruction of surfaces acquired by stereoscopic systems. Our final objective was to develop an acquisition system that provides directly semi-regular output, instead of the classical point clouds ( that must be subsequently cleaned, triangulated, and remeshed, if users want a discretized surface with a semi-regular connectivity).
Our two main contributions have been i) an efficient method of blue noise resampling of surface meshes, and a reconstruction method that directly generates a semi-regular mesh from stereoscopic images.

One Related Publication:

J.-L. Peyrot, F. Payan, M. Antonini, From stereoscopic images to semi-regular meshes, in Signal Processing: Image Communication, Volume 40, p. 97-110, doi: 10.1016/j.image.2015.11.004, January, 2016.

sr

Semi-Regular Remeshing

I worked on the semi-regular remeshing of surfaces during Aymen Kammoun's PhD (2007-2011) and during a collaboration with Basile Sauvage, (Icube, Strasbourg) and Céline Roudet (Le2i, Dijon).
The semi-regular meshes are based on a regular subdivision connectivity. This subdivision connectivity also allows a compact representation, adapted to multiresolution analysis and wavelet compression. Usually, SR meshes are not provided by current acquisition systems or software. As a consequence, if we want a semi-regular mesh, we have to remesh the data.

One Related Publication:

F. Payan, C. Roudet, B. Sauvage, Semi-regular Triangle Remeshing: a Comprehensive Study, Computer Graphics Forum, Blackwell Publishing, Volume 34, Issue 1, pp.86-102, doi: 10.1111/cgf.12461, February, 2015.