Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees
Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees
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DOI：
10.1007/s00371-020-01966-7
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Jules Morel;A. Bac;T. Kanai
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作者：
Jules Morel;A. Bac;T. Kanai
三维点云数据的分割在非均衡、非均匀数据集的情况下仍然是一个悬而未决的问题。在植物树木建模的应用环境中，一个根本的挑战在于将树叶与木材分离。基于深度学习和类决策过程，我们提出了一种创新的方法，旨在从树木的陆地LiDAR点云中分离叶点和木点。虽然简单，我们的方法学习树的特征点模式有效和鲁棒。为了训练我们的3D深度学习模型，我们构建了不同树种的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一起，在分割任务中比最先进的方法有了显着的改进。
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.