Method of improving density of three-dimensional reconstructed point cloud based on neighborhood block matching

Abstract

The invention discloses a method of improving the density of the three-dimensional reconstructed point cloud based on neighborhood block matching. The method comprises the steps of obtaining the rough and sparse object point cloud using a three-dimensional reconstruction algorithm based on an image sequence, and obtaining the transformation matrix of a camera in the three-dimensional space when each frame of images is taken; processing the original image again, and performing the dense feature matching in the image using a neighborhood-based block matching algorithm; then, according to the obtained position of the camera in the space, carrying out a legitimacy test on the obtained dense feature points, and mapping the feature points satisfying requirements to corresponding positions in the three-dimensional point cloud; and conducting an outer point filtering for the obtained point cloud using an outer point deletion algorithm based on the object contour, and performing a color remapping to obtain the dense point cloud which is far better than the original point cloud. According to the invention, the point cloud having far higher quality and density than that by the traditional algorithm can be obtained, the effect of the original algorithm can be greatly improved, and the quality of reconstruction is improved. The method has high universality and robustness.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for increasing the density of a three-dimensional reconstruction point cloud based on neighborhood block matching. Background technique [0002] 3D reconstruction based on image sequences involves many disciplines, and belongs to the reverse process of camera imaging principles. The research scope mainly includes: object target recognition, feature detection, feature matching and other fields. Since its birth, 3D reconstruction technology has become one of the hot spots and difficulties in the field of computer vision. Its input only requires general color images, and its high universality provides unparalleled convenience when modeling objects in the real world. However, since there are too many parameters that need to be calculated and measured in 3D reconstruction, how to obtain a high-quality reconstruction model has become a difficult problem to...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method for improving the density of 3D reconstruction point cloud based on neighborhood block matching, aiming at solving the problem that the quality of the point cloud obtained

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