Reconstructing Geometrical Models of Indoor Environments Based on Point Clouds

Abstract

In this paper, we present a workflow that combines supervised and unsupervised methods for the reconstruction of geometric models with architectural information from unordered 3D data. Our method uses a downsampling strategy to enrich features to provide scalability for large datasets, increase robustness, and be independent of the sensor used. A Neural Network is then used to segment the resulting point cloud into basic structures. This removes furniture and clutter and preserves the relevant walls, ceilings, floors, and openings. A 2D projection combined with a graph structure is used to find a Region of Interest within the cleaned point cloud, indicating a potential room. Each detected region is projected back into a 3D data patch to refine the room candidates and allow for more complex room structures. The resulting patches are fitted with a polygon using geometric approaches. In addition, architectural features, such as windows and doors, are added to the polygon. To demonstrate that the presented approach works and that the network provides usable results, even with changing data sources, we tested the approach in different real-world scenarios with different sensor systems.