Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rao-BlackWellized particle filters have achieved a breakthrough in the scalability of filters used for Structure from Motion (SFM) and Simultaneous Localization And Mapping (SLAM). The new generations of these filters employ as proposal distribution the optimal <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> , the one taking into consideration not only the previous motion of the camera, but also the most recent measurement. However the way they sample from this importance function is not optimal since the locations of 3-d features are updated using a motion predicted only from the previous state. This results in a performance lower than the Extended Kalman Filters (EKF)s. We propose in this paper an approach that bears similarity with the Random Sample Consensus (RANSAC) paradigm and that enables us to sample more efficiently from the optimal importance function. It allows us to update the depth based on a motion updated using information from the most recent image and hence the updated samples would have a higher chance to be in regions corresponding to high posterior probability. This results in a performance equal to the performance of the EKF with much higher scalability. Also, our samples being generated and updated based on random sampling of the features, this provides an improved robustness to outliers.
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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 it