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DensingQueen: Exploration Methods for Spatial Dense Dynamic Data

Willich, Julius von ; Günther, Sebastian ; Matviienko, Andrii ; Schmitz, Martin ; Müller, Florian ; Mühlhäuser, Max (2023)
DensingQueen: Exploration Methods for Spatial Dense Dynamic Data.
11th ACM Symposium on Spatial User Interaction. Sydney, Australia (13.-15.10.2023)
doi: 10.1145/3607822.3614535
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (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.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2023
Autor(en): Willich, Julius von ; Günther, Sebastian ; Matviienko, Andrii ; Schmitz, Martin ; Müller, Florian ; Mühlhäuser, Max
Art des Eintrags: Bibliographie
Titel: DensingQueen: Exploration Methods for Spatial Dense Dynamic Data
Sprache: Englisch
Publikationsjahr: 13 Oktober 2023
Verlag: ACM
Buchtitel: SUI '23: Proceedings of the 2023 ACM Symposium on Spatial User Interaction
Veranstaltungstitel: 11th ACM Symposium on Spatial User Interaction
Veranstaltungsort: Sydney, Australia
Veranstaltungsdatum: 13.-15.10.2023
DOI: 10.1145/3607822.3614535
URL / URN: https://dl.acm.org/doi/abs/10.1145/3607822.3614535
Kurzbeschreibung (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.

Freie Schlagworte: Spatial Data, Data Interaction, Virtual Reality, Dense Data, Data exploration, Dynamic Data
Zusätzliche Informationen:

Art.No.: 22

Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Telekooperation
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Hinterlegungsdatum: 01 Nov 2023 13:19
Letzte Änderung: 22 Nov 2023 10:47
PPN: 513393064
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