Coglobe
Bibliographic record
Abstract
Fish Tank Virtual Reality (FTVR) creates a compelling 3D illusion for a single person by rendering to their perspective with head-tracking. However, typically, other participants cannot share in the experience since they see a weirdly distorted image when they look at the FTVR display making it difficult to work and play together. To overcome this problem, we have created CoGlobe: a large spherical FTVR display for multiple users. Using CoGlobe, Siggraph attendees will experience the latest advance of FTVR that supports multiple people co-located in a shared space working and playing together through two different multiplayer games and tasks. We have created a competitive two-person 3D Pong game (Figure 1b) for attendees to experience a highly interactive two-person game looking at the CoGlobe. Onlookers can also watch using a variation of mixed reality with a tracked mobile smartphone. Using a smartphone as a second screen registered to the same virtual world enables multiple people to interact together as well. We have also created a cooperative multi-person 3D drone game (Figure 1c) to illustrate cooperation in FTVR. Attendees will also see how effective co-located 3D FTVR is when cooperating on a complex 3D mental rotation (Figure 1d) and a path-tracing task (Figure 1a). CoGlobe overcomes the limited situation awareness of headset VR, while retaining the benefits of cooperative 3D interaction and thus is an exciting direction for the next wave of 3D displays for work and fun for Siggraph attendees to experience.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.546 | 0.396 |
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".