Segmentation of CAD models using hybrid representation
Bibliographic record
Abstract
In this paper, we introduce an innovative method for computer-aided design (CAD) segmentation by concatenating meshes and CAD models. Many previous CAD segmentation methods have achieved impressive performance using single representations, such as meshes, CAD, and point clouds. However, existing methods cannot effectively combine different three-dimensional model types for the direct conversion, alignment, and integrity maintenance of geometric and topological information. Hence, we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations, as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation accuracy. To combine these two model types, our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh models. For complex CAD models, model segmentation is crucial for model retrieval and reuse. In partial retrieval, it aims to segment a complex CAD model into several simple components. The first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh models. The second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD models. This combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object semantics. This study uses the Fusion 360 Gallery dataset. Experimental results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".