Efficient panoramic sampling of real-world environments for image-based stereoscopic telepresence
Bibliographic record
Abstract
A key problem in telepresence systems is how to effectively emulate the subjective experience of being there delivered by our visual system. A step toward visual realism can be achieved by using high-quality panoramic snapshots instead of computer-based models of the scene. Furthermore, a better immersive illusion can be created by enabling the free viewpoint stereoscopic navigation of the scene, i.e. using omnistereoscopic imaging. However, commonly found implementation constraints of telepresence systems such as acquisition time, rendering complexity, and storage capacity, make the idea of using stereoscopic panoramas challenging. Having these constraints in mind, we developed a technique for the efficient acquisition and rendering of omnistereoscopic images based on sampling the scene with clusters of three panoramic images arranged in a controlled geometric pattern. Our technique can be implemented with any off-the-shelf panoramic cameras. Furthermore, it does not require neither the acquisition of additional depth information of the scene nor the estimation of camera parameters. The low the computational complexity and reduced data overhead of our rendering process make it attractive for the large scale stereoscopic sampling in a variety of scenarios.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".