Abstract
In this paper, we present Smart-Tree, a supervised method for approximating the medial axes of branch skeletons from a tree's point cloud. A sparse voxel convolutional neural network extracts each input point's radius and direction towards the medial axis. A greedy algorithm performs robust skeletonization using the estimated medial axis. The proposed method provides robustness to complex tree structures and improves fidelity when dealing with self-occlusions, complex geometry, touching branches, and varying point densities. We train and test the method using a multi-species synthetic tree data set and perform qualitative analysis on a real-life tree point cloud. Experimentation with synthetic and real-world datasets demonstrates the robustness of our approach over the current state-of-the-art method. Further research will focus on training the method on a broader range of tree species and improving robustness to point cloud gaps. The details to obtain the dataset are at this https URL.
Abstract (translated)
在本文中,我们介绍了Smart-Tree,一种监督方法,从树点云中近似分支骨骼的 Medial 轴。一个稀疏的立方点卷积神经网络提取每个输入点半径和向 Medial 轴的方向。一个贪心算法使用估计的 Medial 轴进行稳定的骨骼化。该方法提供了复杂的树结构的稳定性,并改善了与自我包含、复杂几何、接触分支和不同点密度的精度。我们使用多个物种的人工合成树数据集训练和测试方法,并在真实的树点云上进行定性分析。与合成和实际数据集的实验表明,我们的方法比当前最先进的方法更加稳健。进一步的研究工作将关注训练方法以涵盖更广泛的树物种,并改善点云间隙的稳健性。获取数据集详细信息的 URL 如下:
URL
https://arxiv.org/abs/2303.11560