In recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation and classification. In this paper, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM), provide a well suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new neural network architecture outperforming the CNN in learning time, processing time and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train and classifies millions of points in seconds.

中文翻译：

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PCEDNet：用于在 3D 点云中进行快速有效边缘检测的神经网络

近年来，卷积神经网络 (CNN) 已被证明是处理点云的有效分析工具，例如用于重建、分割和分类。在本文中，我们专注于点云中边缘的分类，其中描述了边缘及其周围环境。我们提出了一种新的参数化方法，为每个点添加一组关于以不同尺度重建的周围形状的差分信息。这些参数存储在尺度空间矩阵 (SSM) 中，提供了非常合适的信息，神经网络可以从中学习边缘的描述，并使用它在获取的点云中有效地检测它们。在 SSM 上成功应用多尺度 CNN 以有效分类边缘及其邻域后，我们提出了一种新的神经网络架构，在学习时间、处理时间和分类能力方面都优于 CNN。我们的架构很紧凑，需要小的学习集，在几秒钟内训练和分类数百万个点的速度非常快。