Foveated Stereoscopic Display for the Visualization of Detailed Virtual Environments
Bibliographic record
Abstract
We present a new method for the stereoscopic display of complex virtual environments using a foveated arrangement of four images. The system runs on four rendering nodes and four projectors, for the fovea and periphery in each eye view. The use of high-resolution insets in a foveated configuration is well known. However, its extension to projector-based stereoscopic displays raises a specific issue: the visible boundary between fovea and periphery present in each eye creates a stereoscopic cue that may conflict with the perceived depth of the underlying scene. A previous solution to this problem displaces the boundary in the images to ensure that it is always positioned over stereoscopically corresponding scene locations. The new method proposed here addresses the same problem, but by relaxing the stereo matching criteria and reformulating the problem as one of spatial partitioning, all computations are performed locally on each node, and require a small and fixed amount of post-rendering processing, independent of scene complexity. We discuss this solution and present an OpenGL implementation; we also discuss acceleration techniques using culling and fragments, and illustrate the use of the method on a complex 3D textured model of a Byzantine crypt built using laser range imaging and digital photography.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".