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

PCEDNet : A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds