Patchlets: Representing Stereo Vision Data with Surface Elements
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a class of augmented surface elements which we call patchlets. Patchlets are planar surface elements generated from dense stereo vision 3D range images. Patchlets have a position, surface normal and size. In addition they have confidence measures on the position and normal direction that are based on the sensor accuracy. These confidence measures facilitate their use with probabilistic methods such as clustering for range image segmentation. Patchlets are formed by the projection of a pixel within the stereo image onto a sensed surface. They are surface elements that are constructed directly from the sensor data and can be used as a fundamental sensed-data primitive. We describe patchlet formation from the stereo disparity image, the propagation of errors from the stereo sensor model, and confirm experimentally the patchlet model representation. We provide surface segmentation as a sample patchlet application.
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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 | 0.002 | 0.002 |
| 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