Virtual cubic panorama synthesis based on triangular reprojection
Bibliographic record
Abstract
ABSTRACT Cubic panoramas provide an efficient solution to the implementation of immersive and omnidirectional displays for image‐based virtual navigation. To allow unrestricted navigation through the environment, a system must be capable of constructing novel views given the reference images, typically, cubic panoramas. This paper proposes a processing pipeline for cubic panorama synthesis based on 2D/3D triangular reprojection. This pipeline uses matching features to implement the triangular meshing on cubic panoramas, and introduces the matching line constraint, which significantly reduces the artifacts of straight‐line features in the rendered image and accelerates the division process. The modules of this pipeline take the geometrical characteristics of cubic panorama into account. Thus, it can handle the discontinuities of the cube edges when the triangular mesh covers different faces. Furthermore, these modules could be reconfigured to compose customized pipelines and generate virtual cubic panoramas of different quality based on the amount of features. The performance and efficiency of the proposed pipeline is demonstrated with comparison experiments. Copyright © 2013 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".