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 = 2D, 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
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Authors
Dates
2018-12-06—Published
2017-02-20—Filed