Comparing input methods and cursors for 3D positioning with head-mounted displays
Bibliographic record
Abstract
Moving objects is an important task in 3D user interfaces. In this work, we focus on (precise) 3D object positioning in immersive virtual reality systems, especially head-mounted displays (HMDs). To evaluate input method performance for 3D positioning, we focus on an existing sliding algorithm, in which objects slide on any contact surface. Sliding enables rapid positioning of objects in 3D scenes on a desktop system but is yet to be evaluated in an immersive system. We performed a user study that compared the efficiency and accuracy of different input methods (mouse, hand-tracking, and trackpad) and cursor display conditions (stereo cursor and one-eyed cursor) for 3D positioning tasks with the HTC Vive. The results showed that the mouse outperformed hand-tracking and the trackpad, in terms of efficiency and accuracy. Stereo cursor and one-eyed cursor did not demonstrate a significant difference in performance, yet the stereo cursor condition was rated more favourable. For situations where the user is seated in immersive VR, the mouse is thus still the best input device for precise 3D positioning.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".