Image-based building reconstruction with Manhattan-world assumption

The 3D reconstruction of buildings is a challenging research problem especially for image-based methods due to the absence of textured surfaces and difficulty in detecting high-level architectural structures. In this paper, we present an image-based reconstruction algorithm for efficiently modeling of buildings with the Manhattan-world assumption. The first key component of the algorithm is a clustering of geometric primitives (e.g. stereo points and lines) into sparse planes in Manhattan-world coordinates. The combination of such clustered planes greatly limits the possibility of building models to be reconstructed. In the second stage, we employ the graph-cut minimization to obtain an optimal model based on an energy functional that embeds image consistency, surface smoothness and Manhattanworld constraints. Real world building reconstruction results demonstrate the efficiency of the proposed algorithm in handling large scale data and its robustness against the variety of architectural structures.