Hybrid Tactile/Tangible Interaction for 3D Data Exploration

Besançon, Lonni; Issartel, Paul; Ammi, Mehdi; Isenberg, Tobias

doi:10.1109/tvcg.2016.2599217

Cited by 51 publications

(62 citation statements)

References 76 publications

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“…The physicality of the interaction has also been exploited for time‐dependent data exploration by Sollich et al [SvZP∗16]. Besançon et al [BIAI17b] then evaluated a system combining touch and tangible input with a spatially‐aware tablet for 3D visualization. Their techniques take advantage of both input modalities to improve fluid dynamic researchers’ workflow.…”

Section: Context and Related Workmentioning

confidence: 99%

“…We rely, however, on both touch and tangible inputs, thus use a hybrid tactile/tangible paradigm. While such hybrid techniques were shown to be efficient for data exploration, manipulation, and annotation [SGF∗11, BIAI17b], we focus on the more sophisticated task of 3D spatial selection. We hence use a similar setup but design the interaction to serve the needs of 3D selection.…”

Section: Tangible Selection Brushmentioning

confidence: 99%

“…As in previous work [BIAI17b, LODI16, SvZP∗16, SGF∗11], we combine (a) a spatially‐aware mobile device with touch input and (b) a stationary large display (Fig. 3).…”

Section: Tangible Selection Brushmentioning

confidence: 99%

“…Our technique does not suffer from this drawback: it relies solely on user input and is context‐independent. We use a Project Tango [Tan] tablet's 3D‐self‐tracking and touch screen and use a hybrid interaction paradigm [BIAI17b]: touch input specifies a 2D drawn shape and 3D tablet manipulations extend the 2D shape into 3D by “brushing” the 3D space with the 2D shape. Such techniques are essential because all fully or partially automated approaches use inherent data properties to facilitate a selection.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Touch/Tangible Spatial 3D Data Selection

Besançon

Sereno

et al. 2019

Computer Graphics Forum

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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.

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Section: Context and Related Workmentioning

confidence: 99%

Section: Tangible Selection Brushmentioning

confidence: 99%

“…As in previous work [BIAI17b, LODI16, SvZP∗16, SGF∗11], we combine (a) a spatially‐aware mobile device with touch input and (b) a stationary large display (Fig. 3).…”

Section: Tangible Selection Brushmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Touch/Tangible Spatial 3D Data Selection

Besançon

Sereno

et al. 2019

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Coffey et al (2012) extend the world-in-miniature metaphor (Stoakley et al, 1995) in their interactive slice WIM, through a one-to-one static mapping between interactions performed on multi-touch table and a stereoscopic wall display. Besançon et al (2017) presents a conceptual framework of combining tactile input and tangible sensing using a tablet for 3D manipulation tasks. This research provides intriguing techniques for interacting with 3D visualizations of data, in ways that allow the users to almost "touch" the data through the tactile props or devices.…”

Section: Tactile and Commodity 3d User Interfaces For Visualizationmentioning

confidence: 99%

Bare-Hand Volume Cracker for Raw Volume Data Analysis

2016

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Analysis of raw volume data generated from different scanning technologies faces a variety of challenges, related to search, pattern recognition, spatial understanding, quantitative estimation, and shape description. In a previous study, we found that the volume cracker (VC) 3D interaction (3DI) technique mitigated some of these problems, but this result was from a tethered glove-based system with users analyzing simulated data. Here, we redesigned the VC by using untethered bare-hand interaction with real volume datasets, with a broader aim of adoption of this technique in research labs. We developed symmetric and asymmetric interfaces for the bare-hand VC (BHVC) through design iterations with a biomechanics scientist. We evaluated our asymmetric BHVC technique against standard 2D and widely used 3DI techniques with experts analyzing scanned beetle datasets. We found that our BHVC design significantly outperformed the other two techniques. This study contributes a practical 3DI design for scientists, documents lessons learned while redesigning for bare-hand trackers and provides evidence suggesting that 3DI could improve volume data analysis for a variety of visual analysis tasks. Our contribution is in the realm of 3D user interfaces tightly integrated with visualization for improving the effectiveness of visual analysis of volume datasets. Based on our experience, we also provide some insights into hardware-agnostic principles for design of effective interaction techniques.

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Interaction for Immersive Analytics

Büschel

Chen

Dachselt

et al. 2018

Lecture Notes in Computer Science

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In this chapter, we briefly review the development of natural user interfaces and discuss their role in providing human-computer interaction that is immersive in various ways. Then we examine some opportunities for how these technologies might be used to better support data analysis tasks. Specifically, we review and suggest some interaction design guidelines for immersive analytics. We also review some hardware setups for data visualization that are already archetypal. Finally, we look at some emerging system designs that suggest future directions.

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Hybrid Tactile/Tangible Interaction for 3D Data Exploration

Cited by 51 publications

References 76 publications

Hybrid Touch/Tangible Spatial 3D Data Selection

Hybrid Touch/Tangible Spatial 3D Data Selection

Bare-Hand Volume Cracker for Raw Volume Data Analysis

Interaction for Immersive Analytics

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