Constraint-Based Spectral Space Template Deformation for Ear Scans
Bibliographic record
Abstract
Ears are complicated shapes and contain a lot of folds. It is difficult to correctly deform an ear template to achieve the same shape as a scan, while avoiding the reconstruction of noise from the scan and being robust to bad geometry found in the scan. We leverage the smoothness of the spectral space to help in the alignment of the semantic features of the ears. Edges detected in image space are used to identify relevant features from the ear that we align in the spectral representation by iteratively deforming the template ear. We then apply a novel reconstruction that preserves the deformation from the spectral space while reintroducing the original details. A final deformation based on constraints considering surface position and orientation deforms the template ear to match the shape of the scan. We tested our approach on many ear scans and observed that the resulting template shape provides a good compromise between complying with the shape of the scan and avoiding the reconstruction of the noise found in the scan. Furthermore, our approach was robust enough to scan meshes exhibiting typical bad geometry such as cracks and handles.
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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 | 0.000 |
| 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".