DensingQueen: Exploration Methods for Spatial Dense Dynamic Data

Julius von Willich, Sebastian Günther, Andrii Matviienko, Martin Schmitz, Florian Müller, Max Mühlhäuser

SUI 2023

Proceedings of the 2023 ACM Symposium on Spatial User Interaction

TL;DR

What we did: We designed and evaluated two interaction modalities, flashlight and 3D cutting planes, for exploring Spatial Dense Dynamic Data in Virtual Reality.

What we found: We found that the flashlight modality improved tracking of dynamic targets, while the cutting plane modality was more effective for highlighting static volumes of interest, particularly in high-density environments.

Takeaway: Our research contributes practical interaction techniques that significantly enhance user performance and reduce mental load when navigating complex and dense 3D data in Virtual Reality.

Abstract

Research has proposed various interaction techniques to manage the occlusion of 3D data in Virtual Reality (VR), e.g., via gradual refinement. However, tracking dynamically moving data in a dense 3D environment poses the challenge of ever-changing occlusion, especially if motion carries relevant information, which is lost in still images. In this paper, we evaluated two interaction modalities for Spatial Dense Dynamic Data (SDDD), adapted from existing interaction methods for static and spatial data. We evaluated these modalities for exploring SDDD in VR, in an experiment with 18 participants. Furthermore, we investigated the influence of our interaction modalities on different levels of data density on the users’ performance in a no-knowledge task and a prior-knowledge task. Our results indicated significantly degraded performance for higher levels of density. Further, we found that our flashlight-inspired modality successfully improved tracking in SDDD, while a cutting plane-inspired approach was more suitable for highlighting static volumes of interest, particularly in such high-density environments.