A robust method for registration and segmentation of multiple range images
Why this work is in the frame
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Bibliographic record
Abstract
Registration and segmentation of multiple range images are one of the most important problems in range image analysis. This problem has been investigated by a number of researchers, but most of existing methods are easily affected by outlying points (outliers) like noise and occlusion. We first propose a robust method of estimating rigid motion parameters from a pair of range images. This method is an integration of the iterative closest point (ICP) algorithm with the random sampling and the least median of squares (LMS) estimator. We then detect the outliers by thresholding the residuals in the LMS estimation, and finally we classify each pixel into one of five categories to obtain a segmentation. We experimented on real range images taken by two kinds of rangefinders, and observed that our method worked successfully even for noisy data. The proposed method has another advantage of reducing the computational cost.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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