The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model) process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of results of the 3D reconstruction. Additionally, quality indexes are introduced to inspect the results in order to be able to detect potential errors of reconstruction.

https://hal.archives-ouvertes.fr/hal-02320638/document

From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

We present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds with oriented normals by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds.

Automatic reconstruction of fully volumetric 3D building models from oriented point clouds

Digital twin and its implementations in the civil engineering sector

In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better interpret the object under study. Indeed, the high complexity of 3D data along with the large diversity of heritage assets themselves have constituted segmentation and classification methods as currently active research topics. Although machine learning methods brought great progress in this respect, few advances have been developed in relation to cultural heritage 3D data. Starting from the existing literature, this paper aims to develop, explore and validate reliable and efficient automated procedures for the classification of 3D data (point clouds or polygonal mesh models) of heritage scenarios. In more detail, the proposed solution works on 2D data (“texture-based” approach) or directly on the 3D data (“geometry-based approach) with supervised or unsupervised machine learning strategies. The method was applied and validated on four different archaeological/architectural scenarios. Experimental results demonstrate that the proposed approach is reliable and replicable and it is effective for restoration and documentation purposes, providing metric information e.g. of damaged areas to be restored.

/pdf/classification-of-3d-digital-heritage-4b1s02lj1k.pdf

Classification of 3D Digital Heritage

Automated digital modeling of existing buildings: A review of visual object recognition methods

The Architectural, Engineering and Construction (AEC) industry is looking to integrate Building Information Modeling (BIM) for existing buildings. Currently these as-built models are created manually, which is time-consuming. An important step in the automated Scan-to-BIM procedure is the interpretation and classification of point cloud data. This is computationally challenging due to the sheer size of point cloud data for an entire building. Additionally, the variety of objects makes classification problematic. Existing methods integrate prior knowledge from the sensors or environment to improve the results. However, these approaches are therefore often case specific and thus have limited applicability. The goal of this research is to provide a method that is independent of any sensor or scene within a building environment. Furthermore, our method processes the entire building simultaneously, resulting in more distinct local and contextual features. This paper presents a generic approach to automatically identify structural elements for the purposes of Scan-to-BIM. More specifically, a Random Forests classifier is employed for the classification of the floors, ceilings, roofs, walls and beams. As input, our algorithm takes a set of planar primitives that are pre-segmented from the point cloud. This significantly reduces the data while maintaining accuracy. Both contextual and geometric features are used to describe the observed patches. The algorithm is evaluated using realistic data for a wide variety of existing buildings including houses, school facilities, a factory, a castle and a church. The experiments prove that the proposed algorithm is capable of properly labeling 87% of the structural elements with an average precision of 85% in highly cluttered environments without the support of the sensors position. In future work, the classified patches will be processed by class-specific reconstruction algorithms to create BIM geometry.

Classification of sensor independent point cloud data of building objects using random forests

/pdf/automated-semantic-labelling-of-3d-vector-models-for-scan-to-4w7ezpqcm9.pdf

Automated Semantic Labelling of 3D Vector Models for Scan-to-BIM

With the increasing popularity of as-built Building Information Modelling (BIM) for existing buildings, the demand for highly accurate and dense point cloud data is rising. However, the current data acquisition methods are labor intensive and time consuming. Among other factors, the use of total station measurements to establish survey control, is a major cost behind data acquisition workflows. Over the recent years, there have been major innovations in the fields of surveying and robotics, such as the development of Indoor Mobile Mapping systems (IMMS). With these technological advancements, more cost-effective workflows for capturing existing buildings can be realized. In this paper, several state-of-the-art data acquisition techniques and workflows are discussed for Architectural, Engineering and Construction (AEC) industry buildings. Furthermore, a workflow is proposed with a standalone terrestrial laser scanner capturing data in high overlap, using loop-closure and optimization algorithms to guaranty accuracy. Real life test cases of AEC industry buildings are presented, proving that the proposed workflow can provide highly dense point cloud data within project specifications more efficiently.

Bjorn Van Genechten

Papers

Classification of sensor independent point cloud data of building objects using random forests

Automated Semantic Labelling of 3D Vector Models for Scan-to-BIM

Evaluation of data acquisition techniques and workflows for Scan to BIM