The DOI matches its Crossref record.
Development models of herbaceous plants for computer imagery purposes
Open accessAbstract
In this paper we present a method for modeling herbaceous plants, suitable for generating realistic plant images and animating developmental processes. The idea is to achieve realism by simulating mechanisms which control plant growth in nature. The developmental approach to the modeling of plant architecture is extended to the modeling of leaves and flowers. The method is expressed using the formalism of L-systems.
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Citations
Cited by
Artificial evolution for computer graphics
Sims, K. (1991) · Proceedings of the 18th annual conference on Computer graphics and interactive techniques · doi:10.1145/122718.122752 · Cited by 741
Artificial evolution for computer graphics
Sims, K. (1991) · ACM SIGGRAPH Computer Graphics · doi:10.1145/127719.122752 · Cited by 435
Model Synthesis: A General Procedural Modeling Algorithm
Merrell, P., & Manocha, D. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.112 · Cited by 76
Metadata
- Authors
- Prusinkiewicz, P., Lindenmayer, A., & Hanan, J.
- Venue
- ACM SIGGRAPH Computer Graphics
- Published
- 1988-06
- Volume
- 22
- Issue
- 4
- Pages
- 141-150
- ISSN
- 0097-8930 (ISSN-L)
- OpenAlex
- W2103055948
- Cited by
- 249
- References
- 0
- FWCI
- 13.12
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Development models of herbaceous plants for computer imagery purposes. https://app.cochat.ai/papers/10.1145/378456.378503
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- Prusinkiewicz, P., Lindenmayer, A., & Hanan, J. (1988). Development models of herbaceous plants for computer imagery purposes. ACM SIGGRAPH Computer Graphics. https://doi.org/10.1145/378456.378503
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
