TY - GEN

T1 - Point- and Volume-Based Multi-object Acquisition in VR

AU - Wu, Zhiqing

AU - Yu, Difeng

AU - Goncalves, Jorge

N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023

Y1 - 2023

N2 - Multi-object acquisition is indispensable for many VR applications. Commonly, users select a group of objects of interest to perform further transformation or analysis. In this paper, we present three multi-object selection techniques that were derived based on a two-dimensional design space. The primary design dimension concerns whether a technique acquires targets through point-based methods (selecting one object at a time) or volume-based methods (selecting a set of objects within a selection volume). The secondary design dimension examines the mechanisms of selection and deselection (cancel the selection of unwanted objects). We compared these techniques through a user study, emphasizing on scenarios with more randomly distributed objects. We discovered, for example, that the point-based technique was more efficient and robust than the volume-based techniques in environments where the targets did not follow a specific layout. We also found that users applied the deselection mechanism mostly for error correction. We provide an in-depth discussion of our findings and further distill design implications for future applications that leverage multi-object acquisition techniques in VR.

AB - Multi-object acquisition is indispensable for many VR applications. Commonly, users select a group of objects of interest to perform further transformation or analysis. In this paper, we present three multi-object selection techniques that were derived based on a two-dimensional design space. The primary design dimension concerns whether a technique acquires targets through point-based methods (selecting one object at a time) or volume-based methods (selecting a set of objects within a selection volume). The secondary design dimension examines the mechanisms of selection and deselection (cancel the selection of unwanted objects). We compared these techniques through a user study, emphasizing on scenarios with more randomly distributed objects. We discovered, for example, that the point-based technique was more efficient and robust than the volume-based techniques in environments where the targets did not follow a specific layout. We also found that users applied the deselection mechanism mostly for error correction. We provide an in-depth discussion of our findings and further distill design implications for future applications that leverage multi-object acquisition techniques in VR.

KW - Multiple targets

KW - Object selection

KW - Virtual Reality

U2 - 10.1007/978-3-031-42280-5_2

DO - 10.1007/978-3-031-42280-5_2

M3 - Article in proceedings

AN - SCOPUS:85171456577

SN - 9783031422799

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 20

EP - 42

BT - Human-Computer Interaction – INTERACT 2023 - 19th IFIP TC13 International Conference, Proceedings

A2 - Abdelnour Nocera, José

A2 - Kristín Lárusdóttir, Marta

A2 - Petrie, Helen

A2 - Piccinno, Antonio

A2 - Winckler, Marco

PB - Springer

T2 - 19th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2023

Y2 - 28 August 2023 through 1 September 2023

ER -