Abstract
The extraction of permanent structures (such as walls, floors, and ceilings) is an important step in the reconstruction of building interiors from point clouds. These permanent structures are, in general, assumed to be planar. However, point clouds from building interiors often also contain clutter with planar surfaces such as furniture, cabinets, etc. Hence, not all planar surfaces that are extracted belong to permanent structures. This is undesirable as it can result in geometric errors in the reconstruction. Therefore, it is important that reconstruction methods can correctly detect and extract all permanent structures even in the presence of such clutter. We propose to perform semantic scene completion using deep learning, prior to the extraction of permanent structures to improve the reconstruction results. For this, we started from the ScanComplete network proposed by Dai et al. We adapted the network to use a different input representation to eliminate the need for scanning trajectory information as this is not always available. Furthermore, we optimized the architecture to make inference and training significantly faster. To further improve the results of the network, we created a more realistic dataset based on real-life scans from building interiors. The experimental results show that our approach significantly improves the extraction of the permanent structures from both synthetically generated as well as real-life point clouds, thereby improving the overall reconstruction results.
Highlights
Three-dimensional (3D) modeling of building interiors has attracted increased attention in recent years due to the growing demand for realistic 3D models in a wide range of virtual and augmented reality applications
The experimental results show that our approach significantly improves the extraction of the permanent structures from both synthetically generated as well as real-life point clouds, thereby improving the overall reconstruction results
When a building information model is not yet available, one has to be constructed from imagery and/or point clouds
Summary
Three-dimensional (3D) modeling of building interiors has attracted increased attention in recent years due to the growing demand for realistic 3D models in a wide range of virtual and augmented reality applications. These include localization and navigation in public buildings, building construction monitoring or virtual tours in buildings. When a building information model is not yet available, one has to be constructed from imagery and/or point clouds. A building interior model is often still created manually. This is very time-consuming and requires expert knowledge of 3D software tools. Automatic approaches can make the process easier, faster and cheaper
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