An Improved Building Reconstruction Algorithm Based on Manhattan World Assumption and Line-Restricted Hypothetical Plane Fitting

Abstract

An improved patch-based multiview stereo (PMVS) algorithm based on Manhattan world assumption and the line-restricted hypothetical plane fitting method according to buildings’ spatial characteristics is proposed. Different from the original PMVS algorithm, our approach generates seed points purely from 3D line segments instead of using those feature points. First, 3D line segments are extracted using the existing Line3D++ algorithm, and the 3D line segment clustering criterion of buildings is established based on Manhattan world assumption. Next, by using the normal direction obtained using the result of 3D line segment clustering, we propose a multihypothetical plane fitting algorithm based on the mean shift method. Then, through subdividing on the triangle mesh constructed based on the building hypothetical plane model, semidense point cloud can be quickly obtained, and it is used as seed points of the PMVS pipeline instead of the sparse and noisy seed points generated by PMVS itself. After that, dense point cloud can be obtained through the existing PMVS expansion pipeline. Finally, unit and integration experiments are designed; the test results show that the proposed algorithm is 15%∼23% faster than the original PMWS in running time, and at the same time, the reconstruction quality of buildings is improved as well by successfully removing many noise points in the buildings.