Automatic rule generation for procedural modeling of a sketched tree using genetic programming and particle swarm optimization
Bibliographic record
Abstract
Advantages: • Memory efficiency. • Dynamism (i.e. growth process). • Scalability (i.e. growth level). • Portability. • Generating identical trees while preserving the general aspects. Procedural Tree Modeling Proposed Approach Tree modeling is the process of generating realistic 3D models of trees within virtual environments. These models are widely employed in computer animation, game design, and botanology. To address this demand, several approaches such as image-based modeling and procedural modeling have been widely explored. L-system is the most exploited procedural modeling paradigm in this area. It employs a few rules to model the growth of a tree. It has been shown that using high order L-systems leads to realistic 3D tree models. In many cases, only a sketch of a tree is drawn and the rules are not available. Defining a set of rules to reproduce the drawn tree is a tedious task. In this research, a combination of genetic programming and particle swarm optimization is proposed to automatically generate a set of rules from a sketch to model the corresponding tree.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".