3D modeling of laser-scanned trees based on skeleton refined extraction - Jintao Li; Hangbin Wu; Zhihua Xiao; Huaqing Lu (International Journal of Applied Earth Observations and Geoinformation, 2022)
Laser point cloud provides a high-precision data source for the 3D model reconstruction of existing trees. However, due to the complexity of tree structure and the data missing of point cloud, it is difficult to reconstruct complete, detailed and accurate 3D tree models from discrete points, especially the points are incomplete due to single-view scanning. To this end, a new point cloud driven method based on skeleton refined extraction is proposed in this paper. First, as the focus of refined modeling, the main (thick) branches are separated from the raw tree points. Then, the main branch points are over-segmented into some whorl-segments, and the corresponding raw points on the branch surfaces of each whorl-segment are shrunk to generate smooth, continuous and local detail preserved potential skeleton nodes of the main branches. Next, a new shortest path metric meeting the natural growth characteristics of trees is adopted to extract the complete, detailed and correctly connected tree skeleton from both of the shrunk main branch points and the non-main-branch points. After that, to ensure the skeleton centralization, especially that extracted from single-view scanned data, the extracted skeleton is translated by a combination of cylinder fitting and allometric theory. Finally, the complete, detailed and accurate 3D tree model is expressed by a series of cylinders based on the extracted refined skeleton. The proposed method is tested with 7 different tree point clouds, some of which are obtained by single-view scanning and suffer from serious data missing. Both qualitative and quantitative evaluations prove the effectiveness of this method for the refined reconstruction of trees from point clouds. Overall, the average modeling accuracy and its standard deviation of the used different trees are 2.58 cm and 3.20 cm respectively.