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.

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. 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.

The exploitation of offshore windfarms (WFs) goes hand in hand with large capital expenditures (CAPEX) and operational expenditures (OPEX), as these mechanical installations operate continuously for multiple decades in harsh, saline conditions. OPEX can account for up to 30% of the levelised cost of energy (LCoE) for a deployed offshore wind farm. To maintain the cost-competitiveness of deployed offshore WFs versus other renewable energy sources, their LCoE has to be kept in check, both by minimising the OPEX and optimising the offshore wind energy production. As corrosion, in particular uniform corrosion, is a major cause of failure of offshore wind turbine structures, there is an urgent need for corrosion management systems for deployed offshore wind turbine structures (WTs). Despite the fact that initial corrosion protection solutions are already integrated on some critical structural components such as WT towers, WT transition pieces or WT sub-structure (fixed or floating platforms), these components can still be harshly damaged by the corrosive environmental offshore conditions. The traditional preventive maintenance strategy, in which regular manual inspections by experts are necessary, is widely implemented nowadays in wind farm applications. Unfortunately, for such challenging operating environments, regular human inspections have a significant cost, which eventually increase the OPEX. To minimise the OPEX, remote corrosion monitoring solutions combined with supporting software (SW) tools are thus necessary. This paper focuses on the development of a software (SW) tool for the visualisation of corrosion measurement data. To this end, criteria for efficient structural corrosion analysis were identified, namely a scaleable, SCADA-compatible, secure, web accessible tool that can visualise 3D relationships. In order to be effective, the SW tool requires a tight integration with decision support tools. This paper provides three insights: Firstly, through a literature study and non-exhaustive market study, it is shown that a combined visualisation and decision SW tool is currently non-existing in the market. This gap motivates a need for the development of a custom SW tool. Secondly, the capabilities of the developed custom software tool, consisting of a backend layer and visualisation browser designed for this task are demonstrated and discussed in this paper. This indicates that a SCADA-compatible visualisation software tool is possible, and can be a major stepping stone towards a semi-automated decision support toolchain for offshore wind turbine corrosion monitoring.

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