44-Issue 6

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https://diglib.eg.org/handle/10.2312/3607243

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Browsing 44-Issue 6 by Subject "augmented reality"

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    EyeExpand: A Low-Burden and Accurate 3D Object Selection Method With Gaze and Raycasting
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Xu, X.; He, Y.; Ge, Y.; Zheng, Z.; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
    Raycasting is a widely used object selection technique in virtual reality. However, in dense scenes, it becomes difficult for users to accurately select targets when objects are partially or fully occluded. While recent studies have introduced progressive refinement techniques based on raycasting to address these limitations, they still suffer from challenges such as high interaction complexity and difficulties in preserving the relative spatial relationships between objects within the scene. In this paper, we present a simple and efficient 3D progressive refinement technique for object selection in dense scenes while maintaining the relative spatial positions of selected objects. We compare our technique with other progressive refinement techniques and evaluate their performance and user experience in a target selection task within dense VR environments. The results show that in low- and medium-density scenarios, our technique outperforms existing progressive refinement techniques in terms of selection time. In high-density scenarios, the proposed technique significantly reduces physical effort while maintaining comparable selection times, thereby offering an improved overall interactive experience.
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    MARV: Multiview Augmented Reality Visualisation for Exploring Rich Material Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Gall, Alexander; Heim, Anja; Gröller, Eduard; Heinzl, Christoph; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
    Rich material data is complex, large and heterogeneous, integrating primary and secondary non-destructive testing data for spatial, spatio-temporal, as well as high-dimensional data analyses. Currently, materials experts mainly rely on conventional desktop-based systems using 2D visualisation techniques, which render respective analyses a time-consuming and mentally demanding challenge. MARV is a novel immersive visual analytics system, which makes analyses of such data more effective and engaging in an augmented reality setting. For this purpose, MARV includes three newly designed visualisation techniques: MDD Glyphs with a Skewness Kurtosis Mapper, Temporal Evolution Tracker, and Chrono Bins, facilitating interactive exploration and comparison of multidimensional distributions of attribute data from multiple time steps. A qualitative evaluation conducted with materials experts in a real-world case study demonstrates the benefits of the proposed visualisation techniques. This evaluation revealed that combining spatial and abstract data in an immersive environment improves their analytical capabilities and facilitates the identification of patterns, anomalies, as well as changes over time.