Three-dimensional model reestablishment method based on global linear method

Abstract

The invention discloses a three-dimensional model reestablishment method based on a global linear method. According to the method, firstly, the orientation array of all cameras is obtained by solving the relative rotating relations between every two cameras; secondly, stable camera triad reestablishment is obtained through the nonlinear optimization technology, and a linear relation in a triad is solved; thirdly, a system of linear equations is formed according to equations provided by the triad, accurate camera postures can be obtained by solution through a numerical method, and finally, final optimization is carried out on reestablishment results through the technology of concentration of beam adjustment. The three-dimensional model reestablishment method based on the global linear method is fast, robust and accurate through experimental verification.

Description

technical field [0001] The invention relates to a three-dimensional model reconstruction method based on a global linear method, and belongs to the technical field of computer software. Background technique [0002] In recent years, many people have studied the reconstruction of 3D models of scenes using a large number of unordered online photo collections, and structure from motion (sfm) is a key step in these reconstruction processes. The process of 3D reconstruction includes: 1. Extracting image features from all pictures (generally using SIFT feature); 2. Matching feature points (finding common points seen between cameras); 3. Initializing the pose and 3D coordinates of feature points; 4. Adjust camera pose and scene structure (bundle adjustment). In the third step, the traditional method [Snavely06], [Li08], [Agarwal09] adopts incremental bundle adjustment (incremental bundle adjustment) in the initial reconstruction of the camera pose and the three-dimensional coordin...

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Problems solved by technology

However, this method pays too much attention to the geometric constraints between cameras (including the rotation angle and baseline ratio between cameras), and ignores the projection error of feature points. This paper proves that the [jiang2013] method is the average of the camera reconstruction results obtained by triplet optimization. The projection error reaches 30-200, and the reconstruction projection error of the camera triplet is so large. For the reconstruction finally solved by the algorithm, the projection error still exists. For some data sets, because the projection error is too large, even if the cluster adjustment is done Can't get relatively good results

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