Virtual plant has more and more applications in computer games and 3D films. And reconstruction of real plant has caused serious concern in recent years because it could provide us with useful information in computer graphics and forestry. In general, we can acquire the data by 3D laser scanner or digital camera, and make use of the methods in the fields of digital image processing and computer graphics and information in botany to reconstruct the plant model of 3D. Based on the scanned data, the silhouette of tree is extracted, which could provide restriction to the reconstruction of the branches, especially the reconstruction of the twigs, and local location for the reconstruction of the leaves. The number of tree models is limited, but the real trees are in wide range of species and have various shapes. So we propose a method to deform the models of tree, and this method could change the shape of models interactively, make diversity of the shape of tree model in one species. Our contributions are as follows: 1.We propose a method to extract the silhouette of tree crown which is based on the scanned data of real tree. The scanned data is view as whole point cloud, We Delaunay triangulate the point cloud to construct the topological relations of the point cloud, and then compute the interval by the ridii of the every tetrahedron and every triangle in the triangulation. We set the parameter alpha by dichotomy recursively to classify the triangles. If the triangles on the boundary build a manifold surface, we reduce the value of alpha, or else we improve the value of alpha by dichotomy recursively, and then we acquire the optical mesh model of tree crown. The innovative idea of this method is that we improve the alpha-shape method and figure out the optical alpha antomatically, the silhouette extracted is the crown shape of real tree basically. 2.We propose a method to extract the tree shape based on clusters. We choose a root node and compute the distances from the other points to the root. Then we classify the scanned data into two classes: the data of the branches and the data of tree crown. And then we extract the nodes of the skeleton to build the skeleton. The skeleton nodes are classified, and we classify the data of tree crown by the classified nodes of skeleton, and build the shape of the every classified data of tree crown. The advantage of this method is that we make use of the shape of real tree and classify the data of tree crown by cl...
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