A Factorized Recursive Estimation of Structure and Motion from Image Velocities
Bibliographic record
Abstract
We propose a new approach for the recursive estimation of structure and motion from image velocities. The estimation of structure and motion from image velocities is preferred to the estimation from pixel correspondences when the image displacements are small, since the former approach provides a stronger constraint being based on the instantaneous equation of rigid bodies motion. However the recursive estimation when dealing with image velocities is harder than its counterpart (in the case of pixel correspondences) since the number of points is usually larger and the equations are more involved. For this reason, in contrast to the case of point correspondences, the approaches presented so far are mostly limited to assuming a known 3D motion, or estimating the motion and structure independently. The approach presented in this paper introduces a factorized particle filter for estimating simultaneously the 3D motion and depth. Each particle consists of a 3D motion and a set of probability distributions of the depths of the pixels. The recursive estimation is done in three stages. (1) a resampling and a prediction of new samples; (2) a recursive filtering of the individual depths distributions performed using Extended Kalman Filters; and (3)finally a reweighting of the particles based on the image measurement. Results on simulation data show the efficiency of the approach. Future work will focus on incorporating an estimation of object boundaries to be used in a following regularization step.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".