Light stream optimization based three-dimensional reconstruction method and device

Abstract

The invention discloses a light stream optimization based three-dimensional reconstruction method and device. The method comprises the following steps of: collecting a depth picture and a color picture of a multi-viewing angle by utilizing a multi-viewing-angle depth camera, and carrying out three-dimensional spatial alternation so as to obtain a point cloud of each viewing angle; calculating measuring vectors of two groups of point clouds of adjacent viewing angles according to the information of the depth picture and the color picture; establishing a light stream optimization formula according to a multi-channel light stream method by utilizing the measuring vectors of two groups of point clouds of adjacent viewing angles; resolving the light stream optimization formula, thereby obtaining the matching result of two groups of point clouds of the adjacent viewing angles; filling each viewing angle point cloud into a fusion matrix according to the matching result; and processing the fusion matrix, thereby obtaining the reconstruction result. According to the invention, as less as possible of three-dimensional structures of point characterization models can be utilized self-adaptively, thus data redundancy is lowered; meanwhile, the model accuracy is high; and additionally, hardware requirement is not high, and the application range is wide.

Description

technical field [0001] The invention relates to the field of computer graphics, in particular to a three-dimensional reconstruction method and device based on optical flow optimization. Background technique [0002] The main process of the 3D reconstruction algorithm is: firstly collect multi-view pictures of the target, then use adjacent viewing angles for depth estimation to obtain a 3D point cloud of each viewing angle, and finally fuse these point clouds to obtain a complete 3D model. With the continuous development of depth acquisition technology, we can now directly obtain the depth map or point cloud information of any perspective, and the model can be obtained only through point cloud fusion. Multi-view point cloud fusion technology has achieved remarkable results in 3D reconstruction and has also received extensive attention. Point clouds from different perspectives have two major characteristics: the first is redundancy, the point clouds generated by different per...

AI Technical Summary

Problems solved by technology

[0004] The traditional method does not consider that each point cloud comes from different perspectives, so it has corresponding semantic information, so it cannot maximize the redundancy of point clouds from various perspectives.

Moreover, the method of distinguishing conflict points and noise points is relatively simple, and there is no processing method for the global error introduced by inaccurate camera parameters and other reasons.

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