Bibliographic record
Abstract
Evaluating the intrinsic similarities between non-rigid 3D shapes is of vital importance in content-based shape retrieval. In this paper, we present a novel intrinsic embedding technique, the contour canonical form, to express the isometry-invariant shape representation. The basic idea is to generate an unbent mapping shape for each subpart by aligning the geodesic contours. In details, we first extract the feature points on the non-rigid shape. Then, their canonical mapping positions are calculated, which are globally optimized under geodesic constraints defined on the shape surface. Guided by these positions, an embedding shape is finally obtained by adaptively rotating and translating the geodesic contours around the corresponding feature point. Compared with existing spectral embedding methods, our approach excels on both the preservation of geometric information and the computational efficiency. In the experiment, the contour canonical form is applied in retrieving non-rigid 3D shapes from the McGill articulated benchmark. The appealing results clearly demonstrate a significant performance improvement of our approach over state-of-the-art methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".