Exploring interaction with 3D volumetric displays
Bibliographic record
Abstract
Viewing imagery on volumetric displays, which generate true volumetric 3D images by actually illuminating points in 3D space, is akin to viewing physical objects in the real world. These displays typically have a 360° field of view, and the user does not have to wear hardware such as shutter glasses or head-trackers. As such, they are a promising alternative to traditional display systems for viewing in 3D. Although these displays are now commercially available (e.g., www.actuality-systems.com), current applications tend to use them as a non-interactive output-only display device, much like one would use a printer. In order to fully leverage the unique features of these displays, however, it would be desirable if one could directly interact with and manipulate the 3D data being displayed. We investigate interaction techniques for volumetric display interfaces, through the development of an interactive 3D geometric model building application. While this application area itself presents many interesting challenges, our focus is on the interaction techniques that are likely generalizable to interactive applications for other domains. We explore a very direct style of interaction where the user interacts with the virtual data using direct finger manipulations on and around the enclosure surrounding the displayed 3D volumetric image.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".