NEURAL NETWORK ARCHITECTURE FOR PROCESSING OF MULTIDIMENSIONAL POLYLINES AND POLYGONS

Abstract

Certain aspects of the present disclosure provide techniques for representing polylines and polygons. A method generally includes obtaining a ordered set of points that represent a polyline or a polygon in a multidimensional space; forming two or more channels from the ordered set of points, each channel has a respective set of coordinate values that corresponds to a respective coordinate direction in the multidimensional space; inputting the two or more channels into a one-dimensional convolutional neural network (1D CNN); and obtaining, as output from the 1D CNN, a feature vector representation of the polyline or polygon.

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