Scan registration with multi-scale k-means normal distributions transform
Why this work is in the frame
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Bibliographic record
Abstract
The normal distributions transform (NDT) scan registration algorithm has been shown to produce good results, however, has a tendency to converge to a local minimum if the initial parameter error is large. In order to improve the convergence basin for NDT, a multi-scale k-means NDT (MSKM-NDT) variant is proposed. This approach divides the point cloud using k-means clustering and performs the optimization step at multiple scales of cluster sizes. The k-means clustering approach guarantees that the optimization will converge, as it resolves the issue of discontinuities in the cost function found in the standard NDT algorithm. The optimization step of the NDT algorithm is performed over a decreasing scale, which greatly improves the basin of convergence. Experiments show that this approach can be used to register partially overlapping scans with large initial transformation error.
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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