3D-NCuts: Adapting Normalized Cuts to 3D Triangulated Surface Segmentation
Bibliographic record
Abstract
Being able to automatically segment 3D models into meaningful parts is an important goal in 3D shape processing. In this paper, we are proposing a fast and easy-to-implement 3D segmentation approach, which is based on spectral clustering. For this purpose, we define an improved formulation of the similarity matrix which allows our algorithm to segment both free-form and CAD (Computer Aided Design) 3D models. In 3D space, different shapes, such as planes and cylinders, have different surface normal distributions. We defined the similarity of vertices based on their normals which can segment a 3D model into its geometric features. Results show the effectiveness and robustness of our method in segmenting a wide range of 3D models. Even in the case of complex models, our method results in meaningful segmentations. We tested our segmentation approach on real data segmentation, in the presence of noise and also in comparison with other methods which provided good results in all cases.
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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".