3D selection through hand tracking in XR for point clouds using signed distance fields
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2025
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
EuroXR 2025. Proceedings of the application, poster, and demo tracks of the 22nd EuroXR International Conference
Themenheft
DOI der Originalpublikation
Reihe / Serie
VTT Technology
Reihennummer
440
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
234-237
Patentnummer
Verlag / Herausgebende Institution
VTT
Verlagsort / Veranstaltungsort
Winterthur
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Three-dimensional (3D) visualizations are commonly used in many contexts, e.g., medicine, geography, astronomy, and they are gaining ever more popularity with the recent developments in extended reality (XR). While 3D visualizations can offer unique spatial insights and thus are useful in a variety of ways, interacting with them remains challenging despite being of essential importance in working with 3D visualizations. A specific problem with interacting with 3D visualizations is the lack of an effective and efficient way to select data. Traditional selection methods are primarily designed for, and limited by, 2D interfaces, therefore often do not provide the spatial precision, necessary degrees of freedom and sufficient flexibility necessary for effective 3D selection. Existing approaches for 3D are restricted to simple selection volumes, and do not consider optimization large datasets such as point clouds. Given the above, in this paper, we tackle the challenges associated with the 3D selection problem by designing, developing and user testing a GPU-based selection approach utilizing Signed Distance Fields (SDFs). Our approach allows for computationally efficient, realtime selection of near-arbitrary subsets of point clouds visualized in XR environments. Implemented in Unity, the solution combines novel and existing selection techniques i.e., two direct geometry based ones (shapes, convex hull), and two brushing based ones (brush sphere and brush hands).
Schlagwörter
XR, Hand-tracking, Data selection, Point clouds
Fachgebiet (DDC)
Veranstaltung
22nd annual EuroXR conference: EuroXR 2025
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
03.09.2025
Enddatum der Konferenz
05.09.2025
Datum der letzten Prüfung
ISBN
978-951-38-8804-6
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review des Abstracts
Open Access-Status
Closed
Lizenz
Zitation
Fluri, L., & Cöltekin, A. (2025). 3D selection through hand tracking in XR for point clouds using signed distance fields. In K. Helin, B. Schiavi, & E. Tsaknaki (Eds.), EuroXR 2025. Proceedings of the application, poster, and demo tracks of the 22nd EuroXR International Conference (pp. 234–237). VTT. https://doi.org/10.32040/2242-122X.2025.T440