A point cloud hole spiral-filling method based on 2D and 3D data fusion

Abstract

Due to unfavorable factors, such as environmental noise, reflection, and occlusion of a scanned object, a point cloud obtained by structured light measurement equipment contains holes, which limits further application of the three-dimensional data. This study proposes a point cloud hole spiral-filling method based on the fusion of two- and three-dimensional data. The pixel coordinates corresponding to the point cloud are calculated according to the scanning system parameters. Based on a matrix-like distribution pattern of a camera’s pixel coordinates, the missing pixel coordinates are used to locate the hole boundary points, and the pixel coordinates of the filled points are obtained. In addition, the pixel coordinates of the hole boundary points are used as a sampling layer, which can be used to separate the outermost pixel coordinates of the filled points. Similarly, the pixel coordinates of the filling points are helically stratified. The pixel coordinates of the filling points and their neighboring points, as well as the height coordinates of the neighboring points, are used for surface interpolation to estimate the height coordinates of the filling points. The hole filling is realized after the correction of coordinates using the four-connected domain. The experimental results demonstrate that the proposed spiral hole filling algorithm can accurately identify the hole boundary and can estimate the filling points completely while preserving the details of the repair area.