DensingQueen: Exploration Methods for Spatial Dense Dynamic Data

Willich, Julius von ; Günther, Sebastian ; Matviienko, Andrii ; Schmitz, Martin ; Müller, Florian und Mühlhäuser, Max (2023): DensingQueen: Exploration Methods for Spatial Dense Dynamic Data. 11th ACM Symposium on Spatial User Interaction (SUI), Sydney, Australia, 13. - 15. Oktober 2023. Huang, Tony; Sra, Misha; Argelaguet, Ferran; Lopes, Pedro und Machuca, Mayra Barrera (Hrsg.): In: Proceedings of the 2023 ACM Symposium on Spatial User Interaction, 22 New York: Association for Computing Machinery.

Abstract

Research has proposed various interaction techniques to manage the occlusion of 3D data in Virtual Reality (VR), e.g., via gradual refinement. However, tracking dynamically moving data in a dense 3D environment poses the challenge of ever-changing occlusion, especially if motion carries relevant information, which is lost in still images. In this paper, we evaluated two interaction modalities for Spatial Dense Dynamic Data (SDDD), adapted from existing interaction methods for static and spatial data. We evaluated these modalities for exploring SDDD in VR, in an experiment with 18 participants. Furthermore, we investigated the influence of our interaction modalities on different levels of data density on the users’ performance in a no-knowledge task and a prior-knowledge task. Our results indicated significantly degraded performance for higher levels of density. Further, we found that our flashlight-inspired modality successfully improved tracking in SDDD, while a cutting plane-inspired approach was more suitable for highlighting static volumes of interest, particularly in such high-density environments.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 979-8-4007-0281-5
• Ort: New York
• Sprache: Englisch