Accurate floorplan reconstruction using geometric priors

Abstract

We present an accurate and automatic bottom-up floorplan reconstruction method by leveraging geometric priors extracted from raw point clouds of indoor scenes. Compared to two state-of-the-art methods which adopt point density as priors only, our designed geometric priors integrate point density with indoor area recognition and normal information. These geometric priors are used to calculate the confidence score for each unit region as part of the external boundaries. A cost function is developed according to the confidence scores and the normals along a certain edge, as well as the edge length. By minimizing the cost function, we can automatically estimate candidate edges, and connect them edge by edge to generate rough external boundaries. After diminishing the redundant edges, we can finally solve for a fine closed-loop external boundary path via an efficient heuristic method. We validate our method on intensive real indoor scenes. Experimental results show that our method outperforms two state-of-the-art methods in terms of reconstructing accurate external floorplan boundaries.