High Quality Visual Hull Reconstruction by Delaunay Refinement
Bibliographic record
Abstract
In this paper, we employ Delaunay triangulation techniques to reconstruct high quality visual hulls. From a set of calibrated images, the algorithm first computes a sparse set of initial points with a dandelion model and builds a Delaunay triangulation restricted to the visual hull surface. It then iteratively refines the triangulation by inserting new sampling points, which are the intersections between the visual hull surface and the Voronoi edges dual to the triangulation's facets, until certain criteria are satisfied. The intersections are computed by cutting line segments with the visual hull, which is then converted to the problem of intersecting a line segment with polygonal contours in 2D. A barrel-grid structure is developed to quickly pick out possibly intersecting contour segments and thus accelerate the process of intersecting in 2D. Our algorithm is robust, fast, fully adaptive, and it produces precise and smooth mesh models composed of well-shaped tri-angles.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".