Augmenting range data obtained from stereoscopy with model-based image segmentation using planar patches
Bibliographic record
Abstract
For any modern 3D vision guided system, it is imperative to have complete range images for 3D model reconstruction. In practice, depth images obtained from a standard stereo camera can be error-prone with missing depth pixels. This paper proposes a method to augment the range data obtained from stereoscopy with model based image segmentation using planar patches. The method integrates both intensity and range data obtained from a standard stereo system. First, edges are extracted and linked to different segments from the intensity image with the embedded confidence edge detection technique and a general edge-linking algorithm respectively. Since edges are where disparity happens, most straight-line edges segmented from linked edges using a line-curvature extraction algorithm will have valid depth data. The planar patches are then defined by the straight-lines edge. With the knowledge of the planar structure, each depth missing pixels in the region is then determined by various 3-D line equations that pass through the pixel in the world coordinate system. Lastly, the range data in the world coordinate system is converted back into the image coordinate system using a pixel-to-pixel project algorithm. Result demonstrates the accuracy of method for filling up the missing depth in a region.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".