Extending Filter-based Structure from Motion to Large Baselines
Bibliographic record
Abstract
Filter-based Structure from Motion (SfM) approaches work usually in two steps: prediction and update. Prediction is the process of determining a prior distribution of the state vector at time t+1 from the previous distribution at time t. Update is the process of adjusting the predicted distribution so it complies with the new received measurements at time t+1. A key issue in those two steps is that the prediction and update should use statistically independent data and hence the same data can not be used in both of them. In Bayesian SfM filters that maintain a state vector composed of a set of 3D features and of the camera motion, and that use the projections of the 3D features in the images as measurements for the filter, this two step process faces a serious problem in the case where the baseline between successive frames (i.e. the displacement between the camera centers) is wide. This is because the previous estimate of the state vector at time t does not allow to solely determine an estimate of the motion at t+1 accurate enough for the filtering as there would be a significant change of motion between t and t+1. In this paper, we provide a probabilistic solution to this problem by using features that are matched in the last three frames only. We show that this solution provides reliable prediction of the motion across large baselines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".