Supporting Multiple Off-Axis Viewpoints at a Tabletop Display
Bibliographic record
Abstract
A growing body of research is investigating the use of tabletop displays, in particular to support collaborative work. People often interact directly with these displays, typically with a stylus or touch. The current common focus of limiting interaction to 2D prevents people from performing actions familiar to them in the 3D world, including piling, flipping and stacking. However, a problem arises when viewing 3D on large displays that are intended for proximal use; the view angle can be extremely oblique and lead to distortion in the perception of the 3D projection. We present a simplified model that compensates for off-axis viewing for a single user and extend this technique for multiple viewers interacting with the same large display. We describe several implications of our approach to collaborative activities. We also describe other display configurations for which our technique may prove useful, including proximal use of a wall or multiple-display configurations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".