Structure and Motion Recovery Based on Spatial-and-Temporal-Weighted Factorization
Bibliographic record
Abstract
This paper focuses on the problem of structure and motion recovery from uncalibrated image sequences. It has been empirically proven that image measurement uncertainties can be modeled spatially and temporally by virtue of reprojection residuals. Consequently, a spatial-and-temporal-weighted factorization (STWF) algorithm is proposed to handle significant noise contained in the tracking data. This paper presents three novelties and contributions. First, the image reprojection residual of a feature point is demonstrated to be generally proportional to the error magnitude associated with the image point. Second, the error distributions are estimated from a different perspective, that of the reprojection residuals. The image errors are modeled both spatially and temporally to cope with different kinds of uncertainties. Previous studies have considered only the spatial information. Third, based on the estimated error distributions, an STWF algorithm is proposed to improve the overall accuracy and robustness of traditional approaches. Unlike existing approaches, the proposed technique does not require prior information of image measurement and is easy to implement. Extensive experiments on synthetic data and real images validate the proposed method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".