Exploring 3D Interaction in Alternate Control-Display Space Mappings
Bibliographic record
Abstract
The desire to have intuitive, seamless 3D interaction fuels research exploration into new approaches to 3D interaction. However, within these explorations we continue to rely on Brunelleschi's perspective for display and map the interactive control space directly into it without much thought on the effect that this default mapping has. In contrast, there are many possibilities for creating 3D interaction spaces, thus making it important to run user studies to examine these possibilities. Options in mapping the control space to the display space for 3D interaction have previously focused on the manipulation of control-display ratio or gain. In this paper, we present a conceptual framework that provides a more general control-display description that includes mappings for flip, rotation, skew, as well as scale (gain). We conduct a user study to explore 3D selection and manipulation tasks in three of these different mappings in comparison to the commonly used mapping (perspective mapping of control space to a perspective display). Our results show interesting differences between interactions and user preferences in these mappings and indicate that all may be considered viable alternatives. Together this framework and study open the door to further exploration of 3D interaction variations
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".