BaggingHook: Selecting Moving Targets by Pruning Distractors Away for Intention-Prediction Heuristics in Dense 3D Environments | TU Wien

Information

Publication Type: Conference Paper
Workgroup(s)/Project(s): not specified
Date: 2024
ISBN: 9798350374025
Location: Orlando, FL
Lecturer: Alexandre Kouyoumdjian
Event: 2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)
DOI: 10.1109/VR58804.2024.00110
Booktitle: 2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)
Pages: 11
Conference date: 16. March 2024 – 21. March 2024
Pages: 913 – 923
Keywords: Algorithms, AR/VR/Immersive, Human-Subjects Qualitative Studies, Human-Subjects Quantitative Studies, Interaction Design, Mobile, Specialized Input/Display Hardware

Abstract

Selecting targets in dense, dynamic 3D environments presents a significant challenge. In this study, we introduce two novel selection techniques based on distractor pruning to assist users in selecting targets moving unpredictably: BaggingHook and AutoBaggingHook. Both are built upon the Hook intention-prediction heuristic, which continuously measures the distance between the user's cursor and each object to compute per-object scores and estimate the intended target. Our techniques reduce the number of targets in the environment, making heuristic convergence potentially faster. Once pruned away, distractors are also made semi-transparent to reduce occlusion and the overall difficulty of the task. However, their motion is not altered, so that users can still perceive the dynamics of the environment. We designed two pruning approaches: BaggingHook lets users manually prune distractors away, while AutoBaggingHook uses automated, score-based pruning. We conducted a user study in a virtual reality setting inspired by molecular dynamics simulations, featuring crowded scenes of objects moving fast and unpredictably, in 3D. We compared both proposed techniques to the Hook baseline under more challenging circumstances than it had previously been tested. Our results show that AutoBaggingHook was the fastest, and did not lead to higher error rates. BaggingHook, on the other hand, was preferred by the majority of participants, due to the greater degree of control it provides to users, leading some to see entertainment value in its use. This work shows the potential benefits of varying the types of inputs used in intention-prediction heuristics, not just to improve performance, but also to reduce occlusion, overall task load, and improve user experience.

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BibTeX

@inproceedings{boffi-2024-bagginghook,
  title =      "BaggingHook: Selecting Moving Targets by Pruning Distractors
               Away for Intention-Prediction Heuristics in Dense 3D
               Environments",
  author =     "Paolo Boffi and Alexandre Kouyoumdjian and Manuela Waldner
               and Pier Luca Lanzi and Ivan Viola",
  year =       "2024",
  abstract =   "Selecting targets in dense, dynamic 3D environments presents
               a significant challenge. In this study, we introduce two
               novel selection techniques based on distractor pruning to
               assist users in selecting targets moving unpredictably:
               BaggingHook and AutoBaggingHook. Both are built upon the
               Hook intention-prediction heuristic, which continuously
               measures the distance between the user's cursor and each
               object to compute per-object scores and estimate the
               intended target. Our techniques reduce the number of targets
               in the environment, making heuristic convergence potentially
               faster. Once pruned away, distractors are also made
               semi-transparent to reduce occlusion and the overall
               difficulty of the task. However, their motion is not
               altered, so that users can still perceive the dynamics of
               the environment. We designed two pruning approaches:
               BaggingHook lets users manually prune distractors away,
               while AutoBaggingHook uses automated, score-based pruning.
               We conducted a user study in a virtual reality setting
               inspired by molecular dynamics simulations, featuring
               crowded scenes of objects moving fast and unpredictably, in
               3D. We compared both proposed techniques to the Hook
               baseline under more challenging circumstances than it had
               previously been tested. Our results show that
               AutoBaggingHook was the fastest, and did not lead to higher
               error rates. BaggingHook, on the other hand, was preferred
               by the majority of participants, due to the greater degree
               of control it provides to users, leading some to see
               entertainment value in its use. This work shows the
               potential benefits of varying the types of inputs used in
               intention-prediction heuristics, not just to improve
               performance, but also to reduce occlusion, overall task
               load, and improve user experience.",
  isbn =       "9798350374025",
  location =   "Orlando, FL",
  event =      "2024 IEEE Conference Virtual Reality and 3D User Interfaces
               (VR)",
  doi =        "10.1109/VR58804.2024.00110",
  booktitle =  "2024 IEEE Conference Virtual Reality and 3D User Interfaces
               (VR)",
  pages =      "11",
  pages =      "913--923",
  keywords =   "Algorithms, AR/VR/Immersive, Human-Subjects Qualitative
               Studies, Human-Subjects Quantitative Studies, Interaction
               Design, Mobile, Specialized Input/Display Hardware",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/boffi-2024-bagginghook/",
}