Depth Data Reconstruction Based on Gaussian Mixture Model
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
Abstract Depth data is an effective tool to locate the intelligent agent in space because it accurately records the 3D geometry information on the surface of the scanned object, and is not affected by factors like shadow and light. However, if there are many planes in the work scene, it is difficult to identify objects and process the resulting huge amount of data. In view of this problem and targeted at object calibration, this paper puts forward a depth data calibration method based on Gauss mixture model. The method converts the depth data to point cloud, filters the noise and collects samples, which effectively reduces the computational load in the following steps. Besides, the authors cluster the point cloud vector with the Gaussian mixture model, and obtain the target and background planes by using the random sampling consensus algorithm to fit the planes. The combination of target Region Of Intelligent agent (ROI) and point cloud significantly reduces the computational load and improves the computing speed. The effect and accuracy of the algorithm is verified by the test of the actual object.
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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 | 0.001 |
| 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