Evaluating the Performance of Virtual Reality Navigation Techniques for Large Environments
Bibliographic record
Abstract
We present results from two studies comparing the performance of four different navigation techniques (flight, teleportation, world-in-miniature, and 3D cone-drag) and their combinations in large virtual reality map environments. While prior work has individually examined each of these techniques in other settings, our study presents the first direct comparison between them in large open environments, as well as one of the first comparisons in the context of current-generation virtual reality hardware. Our first study compared common techniques (flight, teleportation, and world-in-miniature) for search and navigation tasks. A follow-up study compared these techniques against 3D cone drag, a direct-manipulation navigation technique used in contemporary tools like Google Earth VR. Our results show the strength of flight as a stand-alone navigation technique, but also highlight five specific ways in which viewers can combine teleportation, world-in-miniature, and 3D cone drag with flight, drawing on the relative strengths of each technique to compensate for the weaknesses of others.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".