3D selection through hand tracking in XR for point clouds using signed distance fields
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Authors
Author (Corporation)
Publication date
2025
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editor (Corporation)
Supervisor
Parent work
EuroXR 2025. Proceedings of the application, poster, and demo tracks of the 22nd EuroXR International Conference
Special issue
DOI of the original publication
Series
VTT Technology
Series number
440
Volume
Issue / Number
Pages / Duration
234-237
Patent number
Publisher / Publishing institution
VTT
Place of publication / Event location
Winterthur
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
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).
Keywords
XR, Hand-tracking, Data selection, Point clouds
Subject (DDC)
Event
22nd annual EuroXR conference: EuroXR 2025
Exhibition start date
Exhibition end date
Conference start date
03.09.2025
Conference end date
05.09.2025
Date of the last check
ISBN
978-951-38-8804-6
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the abstract
Open access category
Closed
License
Citation
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