Intelligent Mouse-Based Object Group Selection

Dehmeshki, Hoda; Stuerzlinger, Wolfgang

doi:10.1007/978-3-540-85412-8_4

Cited by 9 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, structure-aware or context-aware selection techniques have been devised that are able to deduce a user's selection intention based on the underlying data and view conditions and obtain a suitable or, at least, approximate selection in the first place. For two-dimensional problems, for example, the perceptual grouping of objects was successfully used for structure-aware selection [14,15]. In three-dimensional space, however, the situation is more complicated.…”

Section: Related Workmentioning

confidence: 99%

CAST: Effective and Efficient User Interaction for Context-Aware Selection in 3D Particle Clouds

Efstathiou

Isenberg

et al. 2016

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

We present a family of three interactive Context-Aware Selection Techniques (CAST) for the analysis of large 3D particle datasets. For these datasets, spatial selection is an essential prerequisite to many other analysis tasks. Traditionally, such interactive target selection has been particularly challenging when the data subsets of interest were implicitly defined in the form of complicated structures of thousands of particles. Our new techniques SpaceCast, TraceCast, and PointCast improve usability and speed of spatial selection in point clouds through novel context-aware algorithms. They are able to infer a user's subtle selection intention from gestural input, can deal with complex situations such as partially occluded point clusters or multiple cluster layers, and can all be fine-tuned after the selection interaction has been completed. Together, they provide an effective and efficient tool set for the fast exploratory analysis of large datasets. In addition to presenting Cast, we report on a formal user study that compares our new techniques not only to each other but also to existing state-of-the-art selection methods. Our results show that Cast family members are virtually always faster than existing methods without tradeoffs in accuracy. In addition, qualitative feedback shows that PointCast and TraceCast were strongly favored by our participants for intuitiveness and efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

CAST: Effective and Efficient User Interaction for Context-Aware Selection in 3D Particle Clouds

Efstathiou

Isenberg

et al. 2016

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…Two-dimensional selection is considerably easier than selection in 3D because the 2D space is fully visible and accessible for interaction. Nevertheless, structure-aware selection that exploits the perceptual grouping of objects may outperform standard selection techniques even in 2D [6,7].…”

Section: Related Workmentioning

confidence: 99%

Efficient Structure-Aware Selection Techniques for 3D Point Cloud Visualizations with 2DOF Input

Efstathiou

Isenberg

et al. 2012

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-Data selection is a fundamental task in visualization because it serves as a pre-requisite to many follow-up interactions. Efficient spatial selection in 3D point cloud datasets consisting of thousands or millions of particles can be particularly challenging. We present two new techniques, TeddySelection and CloudLasso, that support the selection of subsets in large particle 3D datasets in an interactive and visually intuitive manner. Specifically, we describe how to spatially select a subset of a 3D particle cloud by simply encircling the target particles on screen using either the mouse or direct-touch input. Based on the drawn lasso, our techniques automatically determine a bounding selection surface around the encircled particles based on their density. This kind of selection technique can be applied to particle datasets in several application domains. TeddySelection and CloudLasso reduce, and in some cases even eliminate, the need for complex multi-step selection processes involving Boolean operations. This was confirmed in a formal, controlled user study in which we compared the more flexible CloudLasso technique to the standard cylinder-based selection technique. This study showed that the former is consistently more efficient than the latter-in several cases the CloudLasso selection time was half that of the corresponding cylinder-based selection.

show abstract

“…Context-aware and structure-aware techniques rely on a similar concept. These techniques have been shown to be efficient for 2D datasets [DS08,DS09] using the perceptual grouping of objects. The third dimension of most spatial datasets, however, increases the complexity of the problem, since most input paradigms have limited degrees of freedom [YEII16].…”

Section: D Selection For Exploratory Data Analysismentioning

confidence: 99%

Hybrid Touch/Tangible Spatial 3D Data Selection

Besançon

Sereno

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

We discuss 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 propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, 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 describe our approach and present 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.

show abstract

Intelligent Mouse-Based Object Group Selection

Cited by 9 publications

References 11 publications

CAST: Effective and Efficient User Interaction for Context-Aware Selection in 3D Particle Clouds

CAST: Effective and Efficient User Interaction for Context-Aware Selection in 3D Particle Clouds

Efficient Structure-Aware Selection Techniques for 3D Point Cloud Visualizations with 2DOF Input

Hybrid Touch/Tangible Spatial 3D Data Selection

Contact Info

Product

Resources

About