Holotab: Design and Evaluation of Interaction Techniques for a Handheld 3D Light Field Display
Bibliographic record
Abstract
Although the fundamentals of how humans perceive the world in 3D and the techniques used to implement 3D display devices have been discussed for over a century, humans still rely on 2D displays for interaction with almost every digital device. This implies that humans have been missing a significant amount of information when viewing content on conventional 2D displays. This work presents the Holotab system, an interactive light field 3D display - an approach to make a glasses-free 3D light field display using a conventional 2D display and a lenticular sheet. The Holotab system uses 3D content encoded as streams of colour and depth information, then rendered using the Relief Mapping algorithm to ensure adequate performance for real-time applications. An essential type of task in a virtual 3D environment is the ‘path traversing task’, which can be accomplished using a system where users leverage the kinematic chain to perform bimanual input. The users control the camera viewport using the Holotab, and to perform object manipulation using 3D printed tools in parallel. In this study, we found that the performance of our path traversing task depends on the width, the length, and the curvature of a path, regardless of the relative position between the Holotab and the tools.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".