Unsupervised 3D Shape Parsing with Primitive Correspondence
Bibliographic record
Abstract
3D shape parsing, the process of analyzing and breaking down a 3D shape into components or parts, has become an important task in computer graphics and vision. Approaches for shape parsing include segmentation and approximation methods. Approximation methods often represent shapes with a set of primitives fit to the shapes, such as cuboids, cylinders, or superquadrics. However, existing approximation methods typically rely on a large number of initial primitives and aim to maximize their coverage of the target shape, without accounting for correspondences among the primitives. In this paper, we introduce a novel 3D shape approximation method that integrates reconstruction and correspondence into a single objective, providing approximations that are consistent across the input set of shapes. Our method is unsupervised but also supports supervised learning. Experimental results demonstrate that integrating correspondences into the fitting process not only provides consistent correspondences across a set of input shapes, but also improves approximation quality when using a small number of primitives. Moreover, although correspondences are estimated in an unsupervised manner, our method effectively leverages this knowledge, leading to improved approximations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".