<title>Estimation of large-amplitude motion and disparity fields: application to intermediate view reconstruction</title>
Bibliographic record
Abstract
This paper describes a method for establishing dense correspondence between two images in a video sequence (motion) or in a stereo pair (disparity) in case of large displacements. In order to deal with large-amplitude motion or disparity fields, multi-resolution techniques such as blocks matching and optical flow have been used in the past. Although quite successful, these techniques cannot easily cope with motion/disparity discontinuities as they do not explicitly exploit image structure. Additionally, their computational complexity is high; block matching requires examination of numerous vector candidates while optical flow-based techniques are iterative. In this paper, we propose a new approach that addresses both issues. The approach combines feature matching with Delaunay triangulation, and thus reliable long-range correspondences result while the computational complexity is not high (sparse representation). In the proposed approach, feature points are found first using a simple intensity corner detector. Then, correspondence pairs between two images are found by maximizing cross-correlation over a small window. Finally, the Delaunay triangulation is applied to the resulting points, and a dense vector field is computed by planar interpolation over Delaunay triangles. The resulting vector field is continuous everywhere, and thus does not reflect motion or depth discontinuities at object boundaries. In order to improve the rendition of such discontinuities, we propose to further divide Delaunay triangles whenever the displacement vectors within a triangle do not allow good intensity match. The approach has been extensively tested on stereoscopic images in the context of intermediate view reconstruction where the quality of estimated disparity fields is critical for final image rendering. The first results are very encouraging as the reconstructed images are of high quality, especially at object boundaries, and the computational complexity is lower than that of multi- resolution block matching.
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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 | 0.000 |
| 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 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".