Segmenting correlation stereo range images using surface elements
Bibliographic record
Abstract
This work describes methods for segmenting planar surfaces from noisy 3D data obtained from correlation stereo vision. We make use of local planar surface elements called patchlets. Patchlets have 3D position, orientation and size parameters. As well, they have positional confidence measures based on the stereo sensor model. Patchlet orientations (i.e., surface normals) provide important additional dimensionality that reduces the ambiguity of segmentation-by-clustering. Patchlet size allows the use of continuity or coverage constraints when segmenting bounded surfaces from depth images. We use a region-growing approach to identify the number of surfaces that exist in a stereo image and obtain an initial estimate of the surface parameters. We refine segmentation using a maximum likelihood clustering approach that is optimised with Expectation-Maximisation. Confidence measures on the patchlet parameters allow proper weighting of patchlet contributions to the solution. We provide experimental results of the segmentation on complex outdoor scenes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".