Hole-filling for 3D reconstructed models from multi-view stereo

Multi-view based reconstruction is one of the major approaches to create dense 3D point clouds from series of images, and many excellent algorithms have been published recently. Especially, most of the algorithms are capable to achieve complete reconstruction results when dealing with the highly-textured regions of the objects and scenes. However, due to the lack of textures within some regions, surface reflection and occlusions, certain regions of objects or scenes can usually not be reconstructed correctly, leading to undesirable holes in the uncomplete reconstructed point clouds. In this work, a new strategy on hole-filling for 3D reconstructed models from multi-view stereo is proposed. The holes in the point clouds can be detected automatically by examining the geometric properties of the original point clouds, and then the detected holes will be filled by using moving least squares in an iterative manner. The proposed algorithm has been demonstrated to improve the quality and the completeness of reconstructed point clouds testing on DTU dataset in the experiments.