Efficient Roof Vertex Clustering for Wireframe Simplification Based on the Extended Multiclass Twin Support Vector Machine
Why this work is in the frame
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Bibliographic record
Abstract
This study introduces an efficient approach for clustering roof wireframe vertices within the realm of model simplification based on a multiclass twin support vector machine (TWSVM) framework. The proposed method first assigns a dynamic label to each point of the input point cloud, and it then iteratively identifies k cluster center 3-D lines by maintaining short distances between wireframe candidate vertices sharing the same corner. In addition, it ensures that these wireframe candidates from one corner are distanced from the wireframe vertices from the other corners in a drafting roof dataset. This study extends the multiclass TWSVM to tackle the clustering problem of roof wireframe vertices, thus facilitating model simplification. Remarkably, this problem can be solved using a straightforward and efficient iterative algorithm. The results demonstrate that our proposed method achieves more accurate clustering results on 20 out of 24 roof wireframe vertex datasets compared with other relevant methods. Furthermore, the proposed method can efficiently and accurately extract the majority of vertices from roof wireframes in real-world Building3D dataset.
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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 | 0.001 | 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 it