AUTOMATED SEMANTIC SEGMENTATION OF NON-EUCLIDEAN 3D DATA SETS USING DEEP LEARNING

摘要

A computer-implemented method for semantic segmentation of a point cloud comprises receiving a cloud having points representing a vector of an object, preferably part of a dento-maxillofacial structure having a dentition; determining subset(s) including a first number of points arranged around a selected point of the cloud and a second number of points arranged at spatial distances larger than a predetermined spatial distance of the first number of points, the first number of points representing fine feature(s) of the object around the selected point and the second number of points representing object global feature(s); providing each subset of points to a deep neural network, DNN, the DNN being trained to semantically segment points of each subset according to classes associated with the object; and, for each subset point, receiving a DNN output multi-element vector, wherein each element represents a probability that the point belongs to class(es) of the object.