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Cited by 279

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Image-based plant modeling

Quan, Long, Tan, Ping, Zeng, Gang, Yuan, Lu, Wang, Jingdong, Kang, Sing Bing·2006·ACM Transactions on Graphics

Abstract

In this paper, we propose a semi-automatic technique for modeling plants directly from images. Our image-based approach has the distinct advantage that the resulting model inherits the realistic shape and complexity of a real plant. We designed our modeling system to be interactive, automating the process of shape recovery while relying on the user to provide simple hints on segmentation. Segmentation is performed in both image and 3D spaces, allowing the user to easily visualize its effect immediately. Using the segmented image and 3D data, the geometry of each leaf is then automatically recovered from the multiple views by fitting a deformable leaf model. Our system also allows the user to easily reconstruct branches in a similar manner. We show realistic reconstructions of a variety of plants, and demonstrate examples of plant editing.

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Crossref · 10.1145/1141911.1141929

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Metadata

Authors
Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., & Kang, S. B.
Venue
ACM Transactions on Graphics
Published
2006-07
Volume
25
Issue
3
Pages
599-604
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
OpenAlex
W1964396779
Cited by
279
References
14
FWCI
21.61
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Image-based plant modeling. https://app.cochat.ai/papers/10.1145/1141911.1141929
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Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., & Kang, S. B. (2006). Image-based plant modeling. ACM Transactions on Graphics. https://doi.org/10.1145/1141911.1141929

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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.