Semantic segmentation and point cloud processing combined plant recognition and model construction method

Abstract

The invention provides a semantic segmentation and point cloud processing combined plant recognition and model construction method. The method comprises the following steps: 1, generating an orthoimage according to a landscape image obtained by oblique photography; 2, training a deep learning network, and performing semantic segmentation on the orthoimage by a neural network to identify a plant region; 3, generating a point cloud corresponding to the image, and realizing coordinate correspondence between point cloud data and the orthoimage through coordinate system conversion; 4, segmenting the point cloud data to obtain a plant area point cloud; 5, in combination with oblique photography images and point cloud data, plant species are further recognized through k-means point cloud clustering, target detection and other methods; 6, establishing a plant model library; 7, processing the point cloud of the plant area, determining parameters including plant types, positions, sizes and the like, and importing a plant model to replace the point cloud; and 8, converting the plant model into a required format. According to the invention, efficient and accurate recognition of plant species and construction of a three-dimensional plant scene with a sense of reality can be realized.

Description

Technical field [0001] The invention relates to the technical fields of image processing and three-dimensional scene automatic construction, and in particular to a plant identification and model construction method that combines semantic segmentation and point cloud processing. Background technique [0002] The identification of plant species and three-dimensional model construction contained in large-scale landscape images has always been one of the important research contents of virtual reality. The landscape images collected by oblique photography are used for camera calibration, feature extraction, stereo matching, sparse reconstruction, and dense reconstruction. Through reconstruction and other steps, the three-dimensional point cloud data of the scene can be recovered from the two-dimensional image and applied to subsequent model reconstruction, thus serving fields such as virtual reality and environment simulation. To realize scene recognition and scene construction i...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art, and provide a plant recognition and model building method that combines semantic segmentation and point cloud processing to solve the problem that simple semantic segmentation cannot accurately identify plant species and existing plant species contained in oblique photographic images. Some modeling software solves problems such as distortion, deformation, and distortion in the construction of plant scenes, and realizes efficient and accurate identification of plant species and construction of realistic 3D plant scenes

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