Image-based plant modeling

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

326 citations recorded by the source corpus.

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.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/24d517878f0d9d6d1c3836a2a63ad3c9d1ca1a4f

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In Plant Science

Citations in the corpus

Multi-leaf tracking from fluorescence plant videos (2014, background): “Related Work Leaves and plants are common objects of interest in both computer graphics and vision fields, with existing efforts on tasks such as image-based modeling [7, 8], leaf segmentation [5, 9], alignment [4, 10], tracking [6], and retrieval and identification [11–13].”
Leaf segmentation in plant phenotyping: a collation study (2015, background): “The use of color in combination with depth images or multiple images for supervised or unsupervised plant segmentation is also common practice [4,10,29,46,49,51,52,57].”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “The use of color in combination with depth images or multiple images for supervised or unsupervised plant segmentation is also common practice [4,10,29,46,49,51,52,57].”
Automatic reconstruction of tree skeletal structures from point clouds (2010, methodology): “Existing methods take either an interactive [Quan et al. 2006; Tan et al. 2007] or an empirical approach, where the latter heavily relies on heuristics and manual adjustment of parameters [Xu et al. 2007].”