Learning geometric complexes for 3D shape classification
Bibliographic record
Abstract
Geometry and topology are vital elements in discerning and describing the shape of an object. Geometric complexes constructed on the point cloud of a 3D object capture the geometry as well as topological features of the underlying shape space. Leveraging this aspect of geometric complexes, we present an attention-based dual stream graph neural network (DS-GNN) for 3D shape classification. In the first stream of DS-GNN, we introduce spiked skeleton complex (SSC) for learning the shape patterns through comprehensive feature integration of the point cloud’s core structure. SSC is a novel and concise geometric complex comprising principal plane-based cluster centroids complemented with per-centroid spatial locality information. The second stream of DS-GNN consists of alpha complex which facilitates the learning of geometric patterns embedded in the object shapes via higher dimensional simplicial attention. To evaluate the model’s response to different shape topologies, we perform a persistent homology-based object segregation that groups the objects based on the underlying topological space characteristics quantified through the second Betti number. Our experimental study on benchmark datasets such as ModelNet40 and ScanObjectNN shows the potential of the proposed GNN for the classification of 3D shapes with different topologies and offers an alternative to the current evaluation practices in this domain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".