High-resolution insets in projector-based stereoscopic displays: principles and techniques
Bibliographic record
Abstract
We propose a dual-resolution foveated stereoscopic display built from commodity projectors and computers. The technique is aimed at improving the visibility of fine details of 3D models in computer-generated imagery: it projects a high-resolution stereoscopic inset (or fovea, by analogy with biological vision) that is registered in image space with the overall stereoscopic display. A specific issue that must be addressed is the perceptual conflict between the apparent depth of the natural boundary of the projected inset (visible due to changes in color, brightness, and resolution) and that of the underlying scene being displayed. We solve this problem by assigning points to be displayed in either the low resolution display or the inset in a perceptually consistent manner. The computations are performed as a post-processing, are independent of the complexity of the model, and are guaranteed to yield a correct stereoscopic view. The system can accommodate approximately aligned projectors, through image warping applied as part of the rendering pipeline. The method for boundary adjustment is discussed along with implementation details and applications of the technique for the visualization of highly detailed 3-D models of environments and sites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".