Segmentation of 3D point clouds is still an open issue in the case of unbalanced and in-homogeneous data-sets. In the application context of the modeling of botanical trees, a fundamental challenge consists in separating the leaves from the wood. Based on deep learning and a class decision process, we propose an innovative method designed to separate leaf points from wood points in terrestrial LiDAR point clouds of trees. Although simple, our approach learns trees characteristic point patterns efficiently and robustly. To train our 3D deep learning model, we constructed a 3D labeled point cloud data-set of different tree species. Experiments show that our 3D deep representation together with our geometric approach leads to significant improvement over the state-of-the-art methods in segmentation task.

中文翻译：

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不平衡和非均匀点云的分割及其在 3D 扫描树中的应用

在不平衡和不均匀的数据集的情况下，3D 点云的分割仍然是一个悬而未决的问题。在植物树建模的应用环境中，一个基本的挑战在于将叶子与木材分离。基于深度学习和类决策过程，我们提出了一种创新方法，旨在将地面 LiDAR 树木点云中的树叶点与木材点分开。虽然简单，但我们的方法有效且稳健地学习了树的特征点模式。为了训练我们的 3D 深度学习模型，我们构建了一个不同树种的 3D 标记点云数据集。实验表明，我们的 3D 深度表示与我们的几何方法相结合，显着改善了分割任务中最先进的方法。