Tree Skeletonization for Raw Point Cloud Exploiting Cylindrical Shape Prior

Tree skeleton extraction plays a fundamental role in reconstructing both biological and structural models of trees. However, traditional approaches can be ineffective and problematic in guaranteeing the topological correctness and centeredness of the tree skeleton when the tree point clouds contain noise and occlusions. To overcome this limitation, we present a tree skeletonization method to generate topologically correct and well-centered tree skeletons. We extract an initial skeleton from the tree point clouds via an octree and level set method, use cylindrical prior constraint (CPC) optimization and the estimated radii of branches to yield corrected positions of improper joints, and finally obtain updated skeletons with improved smoothness. The good centeredness of our proposed method is intrinsically achieved by
(1)
exploiting the cylindrical shape prior and calculating the CPC in the local neighborhood and
(2)
feeding the prior knowledge regarding the radii of tree branches into a topology refinement algorithm to yield near-optimal estimates of the positions of the skeleton points. To evaluate our method, we construct a novel tree point cloud data set with known ground truth and propose three quantitative assessment methods: skeleton points deviation (SPD), bifurcation points coverage (BPC) and endpoints coverage (EPC). The quantitative assessment and visual assessment show that the proposed method clearly outperforms traditional ones in terms of topology correctness and centeredness of the extracted tree skeleton.