Bibliographic record
Abstract
This paper presents a method for view morphing and interpolation based on triangulation. View morphing here is viewed here as a basic tool for view interpolation. The feature points in each source image are first detected. Based on these feature points, each source image is segmented into a set of triangular regions and local affine transformations are then used for texture mapping of each triangle from the source image to the destination image. This is called triangulation-based texture mapping. However, one of the significant problems associated with this approach is texture discontinuity between adjacent triangles. In order to solve this problem, the triangular patches that might cause these adjacent discontinuities are first detected and the optimal affine transformations for these triangles are then applied. In the subsequent view interpolation step, all source images are transferred to the novel view through view morphing, and the final novel view is the combination of all these candidate novel views. The major improvement over the traditional approach is a feedback-based method proposed to determine the weights for the texture combination from different views. Simulation results show that our method can reduce the discontinuities in triangle-based view morphing and significantly improve the quality of the interpolated views.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".