Data‐guided Authoring of Procedural Models of Shapes
Bibliographic record
Abstract
Abstract Procedural models enable the generation of a large amount of diverse shapes by varying the parameters of the model. However, writing a procedural model for replicating a collection of reference shapes is difficult, requiring much inspection of the original and replicated shapes during the development of the model. In this paper, we introduce a data‐guided method for aiding a programmer in creating a procedural model to replicate a collection of reference shapes. The user starts by writing an initial procedural model, and the system automatically predicts the model parameters for reference shapes, also grouping shapes by how well they are approximated by the current procedural model. The user can then update the procedural model based on the given feedback and iterate the process. Our system thus automates the tedious process of discovering the parameters that replicate reference shapes, allowing the programmer to focus on designing the high‐level rules that generate the shapes. We demonstrate through qualitative examples and a user study that our method is able to speed up the development time for creating procedural models of 2D and 3D man‐made shapes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".