A Comparative Evaluation of Techniques for Locating Out-of-View Targets in Virtual Reality
Bibliographic record
Abstract
In this work, we present the design and comparative evaluation of techniques for increasing awareness of out-of-view targets in virtual reality. We first compare two variants of SOUS-a technique that guides the user to out-of-view targets using circle cues in their peripheral vision-with the existing FlyingARrow technique, in which arrows fly from the user's central (foveal) vision toward the target. fSOUS, a variant with low visual salience, performed well in a simple environment but not in visually complex environments, while bSOUS, a visually salient variant, yielded faster target selection than both fSous and FlyingARrow across all environments. We then compare hybrid techniques in which aspects of SOUS relating to unobtrusiveness and visual persistence were reflected in design modifications made to FlyingARrow. Increasing persistence by adding trails to arrows improved performance but there were concerns about obtrusiveness, while other modifications yielded slower and less accurate target acquisition. Nevertheless, since fSOUS and bSOUS are exclusively for head-mounted display with wide field-of-view, FlyingARrow with trail can still be beneficial for devices with limited field-of-view.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".