Validation of Bim Components by Photogrammetric Point Clouds for Construction Site Monitoring

Abstract

ABSTRACT:Construction progress monitoring is a primarily manual and time consuming process which is usually based on 2D plans and therefore has a need for an increased automation. In this paper an approach is introduced for comparing a planned state of a building (as-planned) derived from a Building Information Model (BIM) to a photogrammetric point cloud (as-built). In order to accomplish the comparison a triangle-based representation of the building model is used. The approach has two main processing steps. First, visibility checks are performed to determine whether or not elements of the building are potentially built. The remaining parts can be either categorized as free areas, which are definitely not built, or as unknown areas, which are not visible. In the second step it is determined if the potentially built parts can be confirmed by the surrounding points. This process begins by splitting each triangle into small raster cells. For each raster cell a measure is calculated using three criteria: the mean distance of the points, their standard deviation and the deviation from a local plane fit. A triangle is confirmed if a sufficient number of raster cells yield a high rating by the measure. The approach is tested based on a real case inner city scenario. Only triangles showing unambiguous results are labeled with their statuses, because it is intended to use these results to infer additional statements based on dependencies modeled in the BIM. It is shown that the label built is reliable and can be used for further analysis. As a drawback this comes with a high percentage of ambiguously classified elements, for which the acquired data is not sufficient (in terms of coverage and/or accuracy) for validation.

Percentage of Completion of in-Situ Cast Concrete Walls Using Point Cloud Data and Bim

“…Typically, the paradigm of detecting the presence of an object is defined as determining whether or not a structural object is built. Tuttas et al (Tuttas et al, 2015) express this information by confirming the presence of BIM objects in consecutive point clouds. They discriminate the geometry of the BIM objects into their boundary surfaces and label each mesh triangle individually based on its Euclidean distance to the point cloud.…”

Unlocking Point Cloud Potential: Fusing MLS Point Clouds With Semantic 3d Building Models While Considering Uncertainty

“…Their approach is based on the work of (Hornung et al, 2013) where a concept of OctoMap had been introduced. Building upon the concept of the space occupation, (Tuttas et al, 2015) introduce validation of BIM elements using photogrammetric point clouds. Also within this work the OctoMap (Hornung et al, 2013) framework is utilized.…”

Visualization of Differences between Spatial Measurements and 3D Planning Data

“…For a comparison of 3D data, Doboš et al [Doboš et al 2018] described a method that recognizes differences between 3D models in the screen space based on different data such as color, depth, normals and texture coordinates and visualizes them for the user. Furthermore, Tuttas et al [Tuttas et al 2014[Tuttas et al , 2017[Tuttas et al , 2015 described an approach that compares point clouds from real scenes with planning data in order to enable automated building documentation. The point clouds are recorded by photogrammetry and Structure from Motion.…”