CADParser: A Learning Approach of Sequence Modeling for B-Rep CAD
Bibliographic record
Abstract
Computer-Aided Design (CAD) plays a crucial role in industrial manufacturing by providing geometry information and the construction workflow for manufactured objects. The construction information enables effective re-editing of parametric CAD models. While boundary representation (B-Rep) is the standard format for representing geometry structures, JSON format is an alternative due to the lack of uniform criteria for storing the construction workflow. Regrettably, most CAD models available on the Internet only offer geometry information, omitting the construction procedure and hampering creation efficiency. This paper proposes a learning approach CADParser to infer the underlying modeling sequences given a B-Rep CAD model. It achieves this by treating the CAD geometry structure as a graph and the construction workflow as a sequence. Since the existing CAD dataset only contains two operations (i.e., Sketch and Extrusion), limiting the diversity of the CAD model creation, we also introduce a large-scale dataset incorporating a more comprehensive range of operations such as Revolution, Fillet, and Chamfer. Each model includes both the geometry structure and the construction sequences. Extensive experiments demonstrate that our method can compete with the existing state-of-the-art methods quantitatively and qualitatively. Data is available at https://drive.google.com/CADParserData.
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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".