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

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.