Bibliographic record
Abstract
Polygonal meshes are ubiquitous in geometric modeling. They are widely used in many applications, such as computer games, computer-aided design, animation, and visualization. One of the important problems in mesh processing and analysis is segmentation, where the goal is to partition a mesh into segments to suit the particular application at hand. In this thesis we study structural-level mesh segmentation, which seeks to decompose a given 3D shape into parts according to human intuition. We take the spectral approach to mesh segmentation. In essence, we encode the domain knowledge of our problems into appropriately-defined matrices and use their eigen-structures to derive optimal low-dimensional Euclidean embeddings to facilitate geometric analysis. In order to build the domain knowledge suitable for structural-level segmentation, we develop a surface metric which captures part information through a volumetric consideration. With such a part-aware metric, we design a spectral clustering algorithm to extract the parts of a mesh, essentially solving a global optimization problem approximately. The inherent complexity of this approach is reduced through sub-sampling, where the sampling scheme we employ is based on the part-aware metric and a practical sampling quality measure. Finally, we introduce a segmentability measure and a salience-driven line search to compute shape parts recursively. Such a combination further improves the autonomy and quality of our mesh segmentation algorithm.
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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 | 0.001 | 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".