Comparing Usability of Augmented Reality and Virtual Reality for Creating Virtual Bounding Boxes of Real Objects
Bibliographic record
Abstract
This study conducts a comparative analysis of user experiences of Augmented Reality (AR) and Virtual Reality (VR) headsets during an interactive semantic mapping task. This task entails the placement of virtual objects onto real-world counterparts. Our investigation focuses on discerning the distinctive features of each headset and their respective advantages within a semantic mapping context. The experiment employs a user interface enabling the creation, manipulation, and labeling of virtual 3D holograms. To ensure parity between the headsets, the VR headset mimics AR by relaying its camera feed to the user. A comprehensive user study, encompassing 12 participants tasked with mapping six tabletop objects, compares interface usability and performance between the headsets. The study participants’ evaluations highlight that the VR headset offers enhanced user-friendliness and responsiveness compared to the AR headset. Nonetheless, the AR headset excels in augmenting environmental perception and interpretation, surpassing VR in this aspect. Consequently, the study underscores that current handheld motion controllers for interacting with virtual environments outperform existing hand gesture interfaces. Furthermore, it suggests potential improvements for VR devices, including an upgraded camera feed integration. Significantly, this experiment unveils the feasibility of leveraging VR headsets for AR applications without compromising user experience. However, it also points to the necessity of future research addressing prolonged usage scenarios for both types of headsets in various interactive tasks.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".