Improving VR Selection using Progressive Refinement with Multi-Touch Marking Menus
Bibliographic record
Abstract
Selection is the process of acquiring targets for subsequent operations (e.g., moving or rotating them) in Virtual Reality (VR) systems.Many selection techniques have been introduced to improve selection performance; nonetheless, 3D selection is still cumbersome.We developed a technique to improve selection time and accuracy concurrently by combining progressive refinement and CountMarks.Our technique, Multi-Touch Progressive Refinement (MTPR), enhanced conventional progressive refinement's quad menu object selection by using CountMarks, a multi-finger touchbased technique previously developed to facilitate marking menu selection on smartphones.In a first user study, we compared our technique with progressive refinement and multi-touch techniques in terms of search time, selection time, and accuracy.The results showed that the multi-touch was fastest and progressive refinement was most accurate.However, we also found that participants were slightly confused by our MTPR technique.Therefore, we enhanced our technique by ordering objects inside menu and labelling them.We ran a second study comparing our Improved Multi-Touch Progressive Refinement Selection Strategy (IMPReSS) with the previous two techniques.The result demonstrated that IMPReSS was both the fastest and most accurate of all the techniques evaluated.iii
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".