基于地面激光点云数据的单木三维重建方法

目的 使用地面三维点云数据,提出一种单株树木三维网格模型重建方法,为精准获取测树因子提供技术支撑。 方法 对获取的点云进行预处理,使用k-d树构建近邻关系图,用Dijkstra算法求算出子图的根。检测出有效路径后,使用探测半径计算关键路径。计算树枝骨架,然后对初始骨架进行Bezier曲线半径平滑,得到平滑的骨架,再将骨架连接,使用半径平滑和圆柱拟合减少点云密度小造成的拟合不足的情况,能够最大限度保留树枝的细节。结果 使用3株落叶松点云数据构建了树枝树干表面网格模型,重建了树木三维结构。将树干、树枝的三维网格模型与点云匹配后,效果较好;所构建的模型能够进行细小枝条的重建,而不是模拟细枝,通过观察重建结果,一级枝的重建效果非常好,大的二级枝也能得到很好的展示;整套算法计算快速,计算时间与枝条的复杂程度、连接关系有关。结论 基于关键路径探测的方法能够很好地构建树木的三维网格模型,可以用于单株树木测树因子的精确提取。

Methodology of individual tree 3D reconstruction based on terrestrial laser scanning point cloud data

【Objective】 The tree measurement in a virtual environment make plant modeling become a continuous concern. The objective of this study is to develop a new methodology for the reconstruction of 3D surface models of individual trees based on TLS point clouds that are expected to obtain precise tree measurement factors. 【Method】 A k-d (k-dimensional) tree was used to build adjacent relations between branches after cloud point preprocessing, and the root of the subgraph was calculated using the Dijkstra algorithm. Furthermore, when the shortest path is established, the critical path is detected by the detection radius. After the branch skeletons were calculated, a Bezier curve was used to smooth the initial radius, and the smoothed skeletons of the branch and trunk were connected. In this situation, the smoothed radius and cylinder fitting were applied to retain the details of the branches if the cloud point density was too small. 【Result】3D surface models of branches and trunks for three larch trees were built, and the tree architecture was reconstructed. The matching of the point cloud and surface models demonstrated good behavior. Subsequently, the small branches were reconstructed instead of simulation, and the model can display a good effect of first-level branches, and bigger second-level branches can also be shown on the screen. The time consumed by this algorithm was extremely fast, and depended on the complexity and connection between branches. 【Conclusion】 The methodology based on critical path detection is capable of reconstruction of 3D tree surface modeling that can be applied for precise measurement of individual trees.