Ifcwall Reconstruction From Unstructured Point Clouds

Abstract

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are processed and the wall topology is reconstructed. However, the preocedure is challenging due to noise, occlusions and the complexity of the input data.<br>In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.

HBIM as Support of Preventive Conservation Actions in Heritage Architecture. Experience of the Renaissance Quadrant Façade of the Cathedral of Seville

“…The experience described in the research is a pioneer in the application of this method, and has allowed the verification of its applicability and possible ways of improvement. In this sense, it can be affirmed that the whole process depends on a capture of high quality data and precise segmentation of these in the line of work already referred to in Section 2.1 as [49,54].…”

“…It implies the fully automated generation of BIM parametric entities from the implementation of a series of algorithms capable of segmenting, hierarchizing and recognizing geometries in the captured data (usually dense point clouds). These mechanisms are still little explored, limiting the experiments carried out based on simple geometries (based on plans) typical of contemporary architecture of rational design [47][48][49]. There is research dedicated to the development of methods that, based on the automatic processing of point clouds, are capable of recognizing and describing more or less complex geometries (from straight lines to quadric surfaces) using reverse engineering technologies [50][51][52][53][54].…”

Indoor 3d Modeling and Flexible Space Subdivision From Point Clouds

“…The process of 3D modelling from the point clouds is not limited to the detection of the permanent structures but also reconstruction of a watertight model. To this end, some researchers reconstruct the walls in a piecewise-planar approach (Mura et al, 2016;Xiao and Furukawa, 2014) while others generate volumetric walls (Bassier et al, 2018;Macher et al, 2017;Ochmann et al, 2016). Our method reconstructs the walls as a volumetric object after detecting the planes in the point clouds.…”

Semi-Automated Topology Adjustment of Partial Wall Geometry

“…This is a highly reliable and accurate representation of the observed structure but it is incomplete due to occlusions and thus can only serve as a static model. Therefore, in a final step, partial LOD200 (BIMForum, 2016) wall geometry is established based on the clustered segments that represents the basic parametric geometry of the walls (Bassier et al, 2018a). The 2D centrelines of these entities serve as the input for the presented method.…”