Polygonal Reconstruction of Building Interiors from Cluttered Pointclouds

In this paper, we propose a framework for reconstructing a compact geometric model from point clouds of building interiors. Geometric reconstruction of indoor scenes is especially challenging due to clutter in the scene, such as furniture and cabinets. The clutter may (partially) hide the structural components of the interior. The proposed framework is able to cope with this clutter by using a hypothesizing and selection strategy, in which candidate faces are firstly generated by intersecting the extracted planar primitives. Secondly, an optimal subset of candidate faces is selected by optimizing a binary labeling problem. We formulate the selection problem as a continuous quadratic optimization problem. Allowing us to incorporate a cost function specifically for indoor scenes. The obtained polygonal surface is not only 2-manifold but also oriented, meaning that the surface normals of each polygon are consistently oriented towards the interior. All adjacent and coplanar faces that were selected, are merged into a single face in order to obtain a final geometric model that is as compact as possible. This compact model of the room uses less memory and allows for faster processing when used in virtual reality applications. The method of L. Nan et al. [8] was used as a starting point for our proposed framework. Finally, as opposed to other state-of-the-art interior modeling approaches, the only input that is required, is the point cloud itself. We do not rely on viewpoint information, nor do we assume constrained input environments with a 2.5D or, more restrictively, a Manhattan-world structure. To demonstrate the practical applicability of our proposed method, we performed various experiments on actual scan data of building interiors.status: publishe