Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Hybrid Touch/Tangible Spatial 3D Data Selection

Participants : Lonni Besançon [Linköpping University] , Mickael Sereno [correspondant] , Lingyun Yu [Hangzhou Dianzi University] , Mehdi Ammi [University of Paris 8] , Tobias Isenberg.

Figure 7. Illustration of Tangible Brush application which combines a spatial-aware multi-touch tablet and a remote large screen which shows different perspectives of the view shared with the tablet.

We discussed spatial selection techniques for three-dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties.

We first proposed a new taxonomy of 3D selection techniques [12], focusing on the amount of control the user has to define the selection volume. We then described the 3D spatial selection technique Tangible Brush (Figure 7), which gives manual control over the final selection volume. It combines 2D touch with 6-DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially-aware tablet. We described our approach and presented its quantitative and qualitative comparison to state-of-the-art structure-dependent selection. Our results show that, in addition to being dataset-independent, Tangible Brush is more accurate than existing dataset-dependent techniques, thus providing a trade-off between precision and effort.