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

Abstract

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.

Hybrid Tactile/Tangible Interaction for 3D Data Exploration

“…For example, Cohé et al's tBox [11], Reismann et al's 3D-RST [56], and Yu et al's FI3D [87] provide dataset navigation facilities, Fu et al's powers-of-10 ladder [19] provides scale navigation at different levels, and Yu et al [85,86] suggest context-aware spatial selection techniques.…”

Hybrid Touch/Tangible Spatial 3D Data Selection

“…CloudLasso was extended by Shan et al [SXL∗14] by analyzing the different clusters created by CloudLasso and only selecting the one with the largest 2D projection. Later, three interactive context‐aware techniques (CAST ) [YEII16] also selected a single connected component, two of which were based on the shape of the drawn lasso. We also use a drawn lasso with our approach but, instead of algorithmically transforming the 2D shape into a selection volume, we allow the user to specify the 3D selection volume directly by means of tangible input (meaning that the visualization 𝓋 , manipulation ℳ , interaction ℐ , and user 𝒰 spaces are collapsed according to Bruckner et al's model of spatial interaction directness [BIRW19]).…”

“…Simple approaches adjust pre‐defined geometric shapes (e.g., [BF07, ASM∗04, SAM∗05, ZBM94]) to give quick selections, but with limited control over the result. Hybrid approaches rely on user input (e. g., 2D drawings on a projection of the 3D data) and the data/view context (e. g., lasso‐based [CSSM06, LBCW05b, ONI05, YEII12, YEII16, YZNC05]). Such techniques provide more control over the result but require more input to specify the selection.…”

“…Aperture [FHZ96], in contrast, allows users to dynamically control the size of the selection cone. Similarly, context‐aware techniques [YEII12, YEII16] allow users to adjust an initially system‐derived selection threshold.…”

Interactive Exploration of Three-Dimensional Scientific Visualizations on Large Display Surfaces

“…Moreover, they (b) do not the shape of drawn lasso itself into account other than to use it as a 2D cut-off constraint for the 3D selection. To address bot issues, Yu et al [89] extended their initial approach an described the CAST family of context-aware selection techniques (Fig. 12).…”