Geometric Deep Learning for Shape Correspondence in Mass Customization
Bibliographic record
Abstract
Abstract Many industries, such as human-centric product manufacturers, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes the flexible design and manufacturing possible. However, personalized designs bring obstacles for the shape matching and analysis, owing to the high complexity and large shape variations. Traditional shape matching methods are limited to shape alignment, which cannot determine the intrinsic in-variance of mass customized models. To extract the deformations widely seen in mass customization paradigm and address the issues of alignment methods in shape matching, we redefine the geometry matching problem as a correspondence problem, and solve for the correspondence of all vertices on a queried shape to a reference shape. A state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected models. Through learning the intrinsic deformations of the products, the underlying variations of the shapes are extracted. We demonstrate the application of the proposed approach in orthodontics industry, and the experimental results show the effectiveness of the proposed method and the defined problem is favorably suitable for shape analysis in mass customization.
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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 | 0.001 |
| 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".