Image - Based Modeling of Plantsand Trees by Sing Bing Kang, Long Quan

By Sing Bing Kang, Long Quan

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It is a typical but challenging example as there are substantial missing points in the crown. Nevertheless, its shape has been recovered reasonably well, with a plausible-looking branch structure. The process was automatic, with the exceptions of manual addition of a branch and a few adjustments to the thickness of branches. 7 is an interesting example: the leaf size relative to the entire tree is moderate and not small as in the other examples. Here, 32 source images were taken along a complete 360◦ path around the tree.

2007), require the tree to be first segmented into branch, leaf, and background regions. Unfortunately, high-quality segmentation is typically difficult to achieve without a significant amount of manual input. Our system does not depend on high quality segmentation. Instead, the user need only draw a few strokes to mark out foliage and visible branches. The segmentation is done using the stroke information and priors on tree structure. User interface. The user draws strokes on the image by moving the mouse cursor and holding a button.

Once the 3D leaf plane has been recovered, we back-project the image boundary onto the leaf plane. We look for the best affine transformation that maps the flat leaf to the image boundary by minimizing the distances between the two sets of points of the boundaries on the leaf plane in space. We adapted the ICP (Iterative Closest-Point) algorithm (Besl and McKay (1992)) to compute the registration of two curve boundaries. We first compute a global affine transformation using the first two components of the SVD decomposition as the initial transformation.

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