Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization

Abstract

We propose a method for urban 3D reconstruction, which incorporates semantic information and plane priors within the reconstruction process in order to generate visually appealing 3D models. We introduce a plane detection algorithm using 3D lines, which detects a more complete and less spurious plane set compared to point-based methods in urban environments. Further, the proposed normalized visibility-based energy formulation eases the combination of several energy terms within a tetrahedra occupancy labeling algorithm and, hence, is well suited for combining it with class specific smoothness terms. As a result, we produce visually appealing and detailed building models (i.e., straight edges and planar surfaces) and a smooth reconstruction of the surroundings.