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 lightweight
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.

本文提出了一种新的参数化方法，使用 Scale-Space 矩阵存储每个点周围不同尺度的重建形状的微分信息，从而提供了足够的信息，使得一个充分的神经网络能够学习边缘，并在获取的点云中高效地检测它们。之后，我们提出了一种新的轻量级神经网络结构，其在学习时间、处理时间和分类能力方面均优于 CNN，具有紧凑结构、需要较少的学习集、训练速度非常快且可以在几秒钟内对数百万个点进行分类。