Projective rectification-based view interpolation for multiview video coding and free viewpoint generation
Why this work is in the frame
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Bibliographic record
Abstract
A projective rectification-based view interpolation algorithm is developed for multiview video coding and free viewpoint video. It first calculates the fundamental matrix between two views without using any camera parameter. The two views are then resampled to have horizontal and matched epipolar lines. One-dimensional disparity is estimated next, which is used to interpolate the image for an intermediate viewpoint. After unrectification, the interpolated view can be displayed directly for free viewpoint video purpose. It can also be used as a reference to encode data of an intermediate camera. Experimental results show that the interpolated views can be 3 dB better than existing method. Video coding results illustrate that the method can provide up to 1.3 dB improvement over JMVC.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it