An exploration of interaction styles in mobile devices for navigating 3d environments
Bibliographic record
Abstract
Large displays are becoming more ubiquitous, but often only present passive information to passerby (e.g., about the 3D layouts and maps of buildings). To improve users' experience, museums and similar places could have a system where users would be able to interactively navigate maps of these public, large buildings to browse quickly what is available and plan their trips so that they are efficient and more enjoyable. Personal touch-based mobile devices can be used effectively as input devices, allowing for opportunistic and serendipitous user interaction. In this paper, we explore the coupling of mobile devices to large displays. We present three interaction styles that enable users to navigate in 3D environments and describe the result of a usability study with the three styles. The results of our study indicate that users prefer a combination of two styles, one supporting discrete, precise motions and the other fluid, continuous movements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".