METHOD OF FORMATION OF NEURAL NETWORK ARCHITECTURE FOR CLASSIFICATION OF OBJECT TAKEN IN CLOUD OF POINTS, METHOD OF ITS APPLICATION FOR TEACHING NEURAL NETWORK AND SEARCHING SEMANTICALLY ALIKE CLOUDS OF POINTS

摘要

FIELD: digital data processing.;SUBSTANCE: invention relates to the field of digital data processing. Stated result is achieved by obtaining a cloud of points of size N = 2^D, describing the object, where D is the depth parameter; forming a kd-tree T of depth D for the obtained point cloud, and the tree contains the root node, leaf nodes and non-leaf nodes; generating for each point of the cloud the feature vector, describing said point; recurrent calculation of the vector of parameters of features describing non-leafing tree nodes, each parameter vector is calculated by combining an elementwise nonlinear transformation and a multiplicative transformation of the feature vectors of the child nodes with a matrix and a free member, determined by the depth of the node and the direction of the partition corresponding to the node in the kd-tree; calculating a feature vector describing the root node of the tree; applying a linear or nonlinear final classifier to the calculated feature vector, predicting the vector of probabilities of attributing an object to a particular semantic class.;EFFECT: technical result is to increase the speed of searching for similar objects by point clouds.;4 cl, 3 dwg