Bidirectional Point Cloud Holes Repair Obtained by SfM and Structured Light

The structure from motion (SfM) algorithm is widely used for point cloud reconstruction. However, one drawback of conventional SfM based methods is that the obtained final point sets may contain holes and noise with rich color information. At the same time, the point cloud collected based on coded structured light is dense and smooth, but lack of color information. To get better point cloud information by combining structured light point cloud and SFM point cloud, this paper proposes a bidirectional point cloud complementation for three-dimensional(3D) holes repairing combining coded structured light and structure from motion (SfM). Firstly, the point feature histogram which is used to automatically extract the point cloud features to determine the initial control point set is constructed according to the surface characteristics. Then, the principal components analysis (PCA) method is used for rough registration, and then the improved iterative nearest point algorithm is used for accurate registration. Secondly, the nearest point search algorithm is used to fuse the color information distributed in the Point cloud obtained by SfM data with the point cloud coordinates of the coded structured light. Finally, obtained by the reverse engineering software Geomagic Studio, the repaired point cloud model is subdivided into triangular meshes to reconstruct the 3D face surface. Experimental results and data analysis show that this method can accurately perform two-way repair fusion on the encoded point cloud obtained by structured light and point cloud obtained by SfM, and realize the hole repair of the face model with the complete surface reconstruction.