Inferring 3D Surface Shape from 2D Contour Curvature
Bibliographic record
Abstract
Boundary shape alone (e.g. in silhouettes) can be a strong perceptual cue to the 3D shape of a smooth object. Certainly the sign of curvature of the bounding contour strongly constrains the 3D shape of the surface at the rim: convex points on the boundary project from convex surface points, whereas concave points project from surface saddle points (Koenderink & van Doorn 1976; Koenderink 1984). Furthermore, when curvature changes smoothly over the surface of an object, these boundary constraints may also carry information about the qualitative shape of the 3D surface interior to the boundary. Here we ask whether the magnitude of curvature of the bounding contour might also contribute to the perceived 3D shape on the interior surface of an object. We generated random 3D shapes by adding Perlin noise to spheres. Objects were partially occluded such that a wedge segment of the object was visible. In separate trials, the bounding contour was either visible or occluded. Observers adjusted the depth of a binocularly viewed dot so that it was perceived to lie on the surface. We found that when direct surface shading and disparity cues were weak, the perceived surface shape on the interior of the object was modulated by the magnitude of curvature of the bounding contour. When direct cues to surface shape were strengthened, this boundary effect was substantially reduced. We conclude that the influence of the bounding contour on the perception of 3D object shape derives not just from the sign of curvature but also its magnitude. We discuss this finding in terms of the ecological statistics of 3D curvature and projected contours of objects in our visual environment. Meeting abstract presented at VSS 2012
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".