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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".