A simplified concentric mosaics system with non-uniformly distributed pre-captured images
Bibliographic record
Abstract
In this paper, a simplified implementation of the Concentric Mosaics image-based rendering technique is proposed. The greatest difficulty for an ordinary user in obtaining the pre-captured images for use in the Concentric Mosaics technique is the precise control of the rotation of a long beam. In the proposed Simplified Concentric Mosaics technique, the camera positions where the pre-captured images will be taken are not precisely controlled, but are estimated from the pre-captured images. We use a stereo technique for estimation of camera positions in this special scenario instead of using the traditional camera pose estimation methods of computer vision. The estimation errors have been analyzed and a closed-loop constraint is used to achieve better rotation angle estimation. In addition, a ratio fitting technique is proposed to select good matching features in the special tri-view matching detection scenario, which subsequently improves rotation angle estimation. Another contribution of the paper is a pre-processing step to eliminate or reduce the possible vertical offsets and other distortions in the pre-captured images, which are caused by any possible motions of the camera that deviate from the ideal one. In a column-based view synthesis technique like the proposed method and the conventional Concentric Mosaics technique, these vertical offsets and distortions in the pre-captured images will lower the quality of the synthesized images. Thus our pre-processing can be applied on both the proposed method and the ordinary Concentric Mosaics technique. The pre-captured image data structures of both Concentric Mosaics and the proposed method have been illustrated and the comparison has been made. The proposed technique has a similar data structure and thus a similar rendering algorithm as the conventional Concentric Mosaics technique. As a result, it meets our objective that an ordinary user can obtain the Concentric Mosaics type image data and plug it into a common Concentric Mosaics rendering framework. Simulation results show that the proposed method can achieve good rendering results.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".