The success achieved by deep learning techniques in image labeling has triggered a growing interest in applying deep learning for three-dimensional point cloud classification. To provide better insights into different deep learning architectures and their applications to ALS point cloud classification, this article presents a comprehensive comparison among three state-of-the-art deep learning networks: PointNet++, SparseCNN, and KPConv, on two different ALS datasets. The performances of these three deep learning networks are compared w.r.t. classification accuracy, computation time, generalization ability as well as the sensitivity to the choices of hyper-parameters. Overall, we observed that PointNet++, SparseCNN, and KPConv all outperform Random Forest on the classification results. Moreover, SparseCNN leads to a slightly better classification result compared to PointNet++ and KPConv, while requiring less computation time and memory. At the same time, it shows a better ability to generalize and is less impacted by the different choices of hyper-parameters.

中文翻译：

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机载激光雷达点云分类深度学习方法比较


深度学习技术在图像标记方面取得的成功引发了人们对将深度学习应用于三维点云分类的兴趣日益浓厚。为了更好地了解不同的深度学习架构及其在 ALS 点云分类中的应用，本文在两个不同的 ALS 数据集上对三种最先进的深度学习网络：PointNet++、SparseCNN 和 KPConv 进行了全面比较。这三个深度学习网络的性能在分类精度、计算时间、泛化能力以及对超参数选择的敏感性方面进行了比较。总的来说，我们观察到 PointNet++、SparseCNN 和 KPConv 在分类结果上都优于随机森林。此外，与 PointNet++ 和 KPConv 相比，SparseCNN 的分类结果稍好，同时需要更少的计算时间和内存。同时，它表现出更好的泛化能力，并且受超参数不同选择的影响较小。