Recursive model optimization using ICP and free moving 3D data acquisition
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
We describe a recursive multiresolution algorithm that reconstructs high-resolution and high-accuracy 3D images from low-resolution sparse range images or profiles. The method starts by creating a rough, partial, and potentially distorted estimate of the model of the object from an initial subset of sparse range data; then, using ICP algorithms, it recursively improves and refines the model by adding new range information. In parallel, real-time tracking of the object is performed in order to allow the laser scan to be automatically centered on the object. The end result is the creation of a high-resolution and accurate 3D model of a free-floating object, and real-time tracking of its position. Examples of the method are presented when the object and the 3D camera are moving freely with respect to each other. The system provides high accuracy hand-held laser scanning that does not require complex and costly mechanical scanning apparatus or external positioning devices.
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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