Three-dimensional reconstruction method and system for mixed pictures

Abstract

The embodiment of the invention provides a three-dimensional reconstruction method and a three-dimensional reconstruction system for mixed pictures. The method comprises the steps of classifying all pictures in a mixed picture set through a trained neural network model to acquire a picture set corresponding to each target in a to-be-modeled scene, wherein the mixed picture set includes pictures atdifferent angles of the to-be-modeled scene and characteristic points of multiple target scenes; and performing three-dimensional reconstruction on each target according to the picture set corresponding to each target to obtain a three-dimensional reconstruction image of the to-be-modeled scene. According to the embodiment, because multiple targets are generally included in the mixed pictures, the mixed pictures need to be classified at present to obtain the picture set corresponding to each target, the three-dimensional reconstruction is performed on the target according to the picture set corresponding to each target so as to obtain the three-dimensional reconstruction image of the to-be-modeled scene; and the embodiment is beneficial to the accuracy and extensibility for three-dimensional reconstruction of the target.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of 3D modeling, and in particular to a method and system for 3D reconstruction for mixing images. Background technique [0002] In computer vision, 3D reconstruction refers to recovering the information of the 3D structure of an object from multiple 2D images, and realizing the 3D reconstruction of the external features of the object such as shape and texture. It is the inverse process of perspective imaging. [0003] The current 3D reconstruction methods based on computer vision can be roughly divided into two categories: stereo vision methods and motion restoration methods. No matter which method is used, the essence is to obtain the information on the third dimension of the object, that is, the depth information. The plane information can give two-dimensional information, and the three-dimensional model of the object can be restored by obtaining the information of the third dimensio...

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But it does not require the rest of the background information, so the input required is ideal, but the implementation process is more complicated

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