We proposed an approach for estimating the shape and geometric parameters of the observed objects from a perspective image based on typed elements, perspective geometry methods and convolutional neural networks. The proposed method uses the assumption that the object under study is rigid. A method is proposed for restoring a 3-D model of an observed object from one perspective image using reference objects and typed elements. Semantic segmentation of typed elements allows to set the photometric parameters of the coordinate system attached to the points on the image. According to the calculated photometric parameters and segmentation of the observed object in the image, its parameters and a 3-D model are estimated. The developed method is applicable for calculating 3-D models from a single perspective image in the vicinity of a road (both road and railway) infrastructure, where there are a large number of typed elements.
The paper describes an approach to restoring a three-dimensional model of rigid objects from a single satellite image based on informative classes identified from the results of machine learning, which include railway rails and poles, roofs and walls of buildings, shadows of poles and buildings, and others. The proposed algorithms take into account various conditions for the presence of certain classes in the image, identified by the results of machine learning, as well as the conditions for the absence of metadata on the spatial resolution and spatial orientation of the shooting and the Sun (shooting angle, scanning azimuth, etc.).
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