This paper introduces Smart-Tree, a supervised method for approximating the medial axes of branch skeletons from a tree point cloud. Smart-Tree uses a sparse voxel convolutional neural network to extract the radius and direction towards the medial axis of each input point. A greedy algorithm performs robust skeletonization using the estimated medial axis. Our 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 evaluate Smart-Tree using a multi-species synthetic tree dataset and perform qualitative analysis on a real-world tree point cloud. Our experimentation with synthetic and real-world datasets demonstrates the robustness of our approach over the current state-of-the-art method. The dataset and source code are publicly available.

翻译：本文介绍了Smart-Tree，一种用于从树木点云中逼近枝条骨架中轴的监督学习方法。Smart-Tree采用稀疏体素卷积神经网络，提取每个输入点指向中轴的半径与方向。通过贪心算法利用估计的中轴实现鲁棒的骨架化。该方法对复杂树木结构具有鲁棒性，在处理自遮挡、复杂几何形状、枝条交叉及点密度变化时能够提高保真度。我们使用多物种合成树木数据集评估Smart-Tree，并对真实世界树木点云进行定性分析。在合成与真实数据集上的实验表明，本方法相较于当前最先进方法具有更优的鲁棒性。数据集与源代码均已公开。