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

摘要

The invention relates to a computer-implemented method for semantic segmentation of a point cloud comprising: receiving a point cloud, the point cloud including points representing a vector in a 3D space, the points representing a predetermined object, preferably part of a dento-maxillofacial structure, the dento-maxillofacial structure including a dentition comprising teeth; determining one or more subsets of the point cloud using a non-uniform resampling algorithm, each of the one or more subsets including a first number of points arranged within a predetermined spatial distance of a selected point of the point cloud and a second number of points arranged at spatial distances larger than the predetermined spatial distance, the first number of point representing one or more fine features of the object around the selected point and the second number of points representing one or more global features of the object; providing each of one or more subsets of points to the input of a deep neural network (DNN), the deep neural network being trained to semantically segment points of each of the one or more subsets that is provided to the input of the DNN according to a plurality of classes associated with the object; and, for each point of the subset that is provided to the input of the DNN, receiving at the output of the DNN a multi-element vector, wherein each element of the vector represents a probability that the point belongs to one of the plurality of classes of the object.