One key step to the tree structure study is skeleton processing. Although there are lots of extraction approaches, the existing methods have paid less attention to extraction effectiveness, which highly use redundant points to formulate the skeleton and bring difficulties to the subsequent 3D modeling. This work proposes a four-step framework for the purpose of skeleton extraction. Firstly, candidate skeleton points are filtered from input data based on the spatial slice projection and grouped using the Euclidean distance analysis. Secondly, a key dynamic path optimization step is used to formulate a tree skeleton using the candidate point information. Thirdly, the optimized path is filled by interpolating points to achieve complete skeletons. Finally, short skeletons are removed based on the distance between branching points and ending points, and then, the extraction skeletons are smoothed for improving the visual quality. Our main contribution lies in that we find the global minimization cost path from every point to the root using a novel energy function. The formulated objective function contains a data term to constrain the distance between points and paths, and a smoothness term to constrain the direction continuities. Experimental scenes include three different types of trees, and input point clouds are collected by a portable laser scanning system. Skeleton extraction results demonstrate that we achieved completeness and correctness of 81.10% and 99.21%. respectively. Besides, our effectiveness is up to 79.26%, which uses only 5.82% of the input tree points in the skeleton representation, showing a promising effective solution for the tree skeleton and structure study.
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