Simplification and unfolding of 3D mesh models: review and evaluation of existing tools
Bibliographic record
Abstract
Generation of planar patterns from 3D shapes is required in many fields such as design of airplane wings, car bodies, shoes and textile products. Unfolding and folding processes are common methods to form a 3D shape from 2D surfaces. A 3D mesh surface is segmented in unfolding process to form 2D patches without overlapping. The patches are then used as patterns for cutting materials to fold them back into the 3D shape. A mesh surface can be very complex with a large number of triangles. It is often required to simplify meshes with some preserved geometrical details before the generation of planar patterns for unfolding. This paper reviews and evaluates existing software tools for both mesh simplification and unfolding of 3D shapes. Performance is evaluated for different simplification algorithms implemented in software tools such as Meshlab and Instant-Meshes. The optimal number of meshes is searched for the minimal error. The algorithms are evaluated based on the efficiency and accuracy of the simplified model. Hausdorff Distance measurement is used for the accuracy assessment. The study presents a comprehensive comparison of three different software tools in unfolding for generation of a set of 2D patches from 3D models. The software tools, Pepakura Designer, Blender and SketchUp, are compared in terms of functionalities and performances in the execution time, automation, number of generated 2D patches and editing tools. Case studies are conducted to illustrate the evaluation in both mesh simplifications and unfolding procedures.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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".