Mobile intelligent terminal building rapid identification method based on building three-dimensional model

Abstract

The invention discloses a mobile intelligent terminal building rapid identification method based on a building three-dimensional model, and belongs to the field of computer graphics. The method comprises the following steps: to begin with, obtaining images of the building three-dimensional model from N preset directions, and obtaining histogram information, feature information and geographical location information of the images; then, obtaining shot images of an intelligent terminal camera, obtaining the shooting direction of the intelligent terminal and obtaining the geographical location information of the intelligent terminal; and finally, carrying out building recognition according to the shot images and candidate buildings to screen out the candidate building, the similarity of which is highest, as a target building. The method, taking the city three-dimensional model as the source of building images, and with space position and image recognition mode being combined, realizes rapid and automatic building identification in the mobile device camera, solves the problems of large workload of source image acquisition and low recognition efficiency and the like in the current building recognition field, and provides technical support for applying the augmented reality technology to online / offline experience and city management and the like.

Description

technical field [0001] The invention relates to the field of building identification, in particular to a method for quickly identifying buildings on a mobile intelligent terminal based on a three-dimensional model of the building. Background technique [0002] The existing technology generally collects building images in the four orientations of due north, due east, due south, and due west of the building as the candidate building library. When the histogram is identified, the captured image and each candidate building are respectively Azimuths are compared to identify buildings. On the one hand, there is an angle between the user's shooting orientation and the above four orientations, resulting in a different shooting angle between the captured image and the image in the building library, resulting in a larger difference in the histogram. At this time, the threshold for histogram recognition becomes lower. Using other features to further identify buildings, the amount of c...

AI Technical Summary

Problems solved by technology

On the one hand, there is an angle between the user's shooting orientation and the above four orientations, resulting in a different shooting angle between the captured image and the image in the building library, resulting in a larger difference in the histogram. At this time, the threshold for histogram recognition becomes lower. Using other features to further identify buildings, the amount of calculation increases geometrically

On the other hand, the existing technology recognizes buildings from four directions with large gaps, which is weak in pertinence and low in efficiency

[0003] In addition, the existing technology obtains the building feature library by collecting photos of buildings. In fact, there are a large number of urban buildings, and it is difficult to obtain the building feature library by collecting photos.

[0004] In summary, the prior art histograms have low accuracy in identifying buildings, and the number of samples screened out is large. Subsequent use of other feature recognition to further identify target buildings will bring a greater amount of calculation, and the pertinence is weak and the efficiency is low. And there is a problem that the workload of photo collection is large

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