Pressure-Based Gain Factor Control for Mobile 3D Interaction using Locally-Coupled Devices

Besançon, Lonni; Ammi, Mehdi; Isenberg, Tobias

doi:10.1145/3025453.3025890

Cited by 17 publications

(16 citation statements)

References 50 publications

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“…Recognizing the distinct advantages of different paradigms and devices, researchers have also sought to combine multiple interaction paradigms together. Interaction techniques that combine tactile, tangible and/or mid‐air aspects appear in the research literature in the 1990s and 2000s [Bux95, PTW98, SS99, OBF03, Yee03, DD05, JGAK07, Wil07] and seem to increase in frequency recently [SSD09, CSH*14, MBH14, LODI16, SvZP*16, BIAI17a, BAI17, BSY*19, Bes17]. Early work by Hinckley et al.…”

Section: Defining a Classification Systemmentioning

confidence: 99%

The State of the Art of Spatial Interfaces for 3D Visualization

Besançon

Ynnerman²,

Keefe

et al. 2021

Computer Graphics Forum

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We survey the state of the art of spatial interfaces for 3D visualization. Interaction techniques are crucial to data visualization processes and the visualization research community has been calling for more research on interaction for years. Yet, research papers focusing on interaction techniques, in particular for 3D visualization purposes, are not always published in visualization venues, sometimes making it challenging to synthesize the latest interaction and visualization results. We therefore introduce a taxonomy of interaction technique for 3D visualization. The taxonomy is organized along two axes: the primary source of input on the one hand and the visualization task they support on the other hand. Surveying the state of the art allows us to highlight specific challenges and missed opportunities for research in 3D visualization. In particular, we call for additional research in: (1) controlling 3D visualization widgets to help scientists better understand their data, (2) 3D interaction techniques for dissemination, which are under‐explored yet show great promise for helping museum and science centers in their mission to share recent knowledge, and (3) developing new measures that move beyond traditional time and errors metrics for evaluating visualizations that include spatial interaction.

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Section: Defining a Classification Systemmentioning

confidence: 99%

The State of the Art of Spatial Interfaces for 3D Visualization

Besançon

Ynnerman²,

Keefe

et al. 2021

Computer Graphics Forum

Self Cite

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show abstract

“…Statistical Method. Following APA recommendations [4], we report our analysis using estimation techniques with effect sizes and confidence intervals (i.e., not using p-values) following recent precedents in HCI [12,56]. Our confidence intervals were computed using BCa bootstrapping, and the term effect size here refers to the measured difference of means.…”

Section: Resultsmentioning

confidence: 99%

Scaptics and Highlight-Planes

Prouzeau

Cordeil

Robin

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Three-dimensional scatterplots suffer from well-known perception and usability problems. In particular, overplotting and occlusion, mainly due to density and noise, prevent users from properly perceiving the data. Thanks to accurate head and hand tracking, immersive Virtual Reality (VR) setups provide new ways to interact and navigate with 3D scatterplots. VR also supports additional sensory modalities such as haptic feedback. Inspired by methods commonly used in Scientific Visualisation to visually explore volumes, we propose two techniques that leverage the immersive aspects of VR: first, a density-based haptic vibration technique (Scaptics) which provides feedback through the controller; and second, an adaptation of a cutting plane for 3D scatterplots (Highlight-Plane). We evaluated both techniques in a controlled study with two tasks involving density (finding high-and lowdensity areas). Overall, Scaptics was the most time-efficient and accurate technique, however, in some conditions, it was outperformed by Highlight-Plane.

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“…A toggle button (right) controls whether selection extrusion uses all 6 DOF of the tangible interaction or whether it is constrained to the tablet's normal axis. A slider (right) reflects the current CD factor obtained from pressure sensors on the tablet's back, similar to Besançon et al's [BAI17] design. In addition, we added a classical slider-based control of the CD factor.…”

Section: Setup and User Interfacementioning

confidence: 99%

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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Pressure-Based Gain Factor Control for Mobile 3D Interaction using Locally-Coupled Devices

Cited by 17 publications

References 50 publications

The State of the Art of Spatial Interfaces for 3D Visualization

The State of the Art of Spatial Interfaces for 3D Visualization

Scaptics and Highlight-Planes

Hybrid Touch/Tangible Spatial 3D Data Selection

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