Genuage: visualizing and analyzing multidimensional point cloud data in virtual reality

Thomas Blanc (1) , Mohamed El Beheiry (2, 1) , Jean-Baptiste Masson (2) , Bassam Hajj (1)

1 PCC - Physico-Chimie-Curie
2 Décision et processus Bayesiens - Decision and Bayesian Computation

The quantity of experimentally recorded point cloud data, such generated in single-molecule experiments, is increasing continuously in both size and dimension. Gaining an intuitive understanding of the data and facilitating multi-dimensional data analysis remains a challenge. It is especially challenging when static distribution properties are not predictive of dynamical properties. Here, we report a new open-source software platform, Genuage, that enables the easy perception, interaction and analysis of complex multidimensional point cloud datasets by leveraging virtual reality. We illustrate the benefit of the Genuage with examples of three-dimensional static and dynamic localization microscopy datasets, as well as some synthetic datasets. Genuage has a large breadth of usage modes, due to its compatibility with arbitrary multidimensional data types extending beyond the single-molecule research community.

Domaines: Informatique [cs]; Physique [physics]

Thomas Blanc, Mohamed El Beheiry, Jean-Baptiste Masson, Bassam Hajj. Genuage: visualizing and analyzing multidimensional point cloud data in virtual reality. 2020. ⟨pasteur-02872314⟩