An examination of mobile phone pointing in surface mapped spatial augmented reality
Bibliographic record
Abstract
We investigate mobile phone pointing in Spatial Augmented Reality (SAR), where digital content is mapped onto the surfaces of a real physical environment. Three pointing techniques are compared: raycast, viewport, and direct. A first experiment examines these techniques in a realistic five-projector SAR environment with representative targets distributed across different surfaces. Participants were permitted free movement, so variations in target occlusion and target view angle occurred naturally. A second experiment validates and further generalizes findings by strictly controlling target occlusion and view angle in a simulated SAR pointing task using an AR HMD. Overall, results show raycast is fastest for non-occluded targets, direct is most accurate, and fastest for occluded targets in close proximity , and viewport falls in between. Using the experiment data, we formulate and evaluate a new Fitts’ model combining two spatial configurations in a SAR pointing task to capture key characteristics, initial target occlusion, target view angle, and user movement. Analysis shows it is a better predictor than previous models.
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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 | 0.002 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".