Enabling BIM for Property Management of Existing Buildings Based on Automated As-is Capturing

Becker, Ralf; Lublasser, Elisa; Martens, Jan; Wollenberg, Raymond; Zhang, Haowei; Brell-Çokcan, Sigrid; Blankenbach, Jörg

doi:10.22260/isarc2019/0028

Cited by 9 publications

(6 citation statements)

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“…This is expressed by the furthest FP point. For modeling on the basis of point clouds or the representation of the recorded objects by symbols, as is the case at the LoD 100 for a BIM application [90], the parameter SD of FP points is more meaningful.…”

Section: Ssr =mentioning

confidence: 99%

Evaluating the Quality of Semantic Segmented 3D Point Clouds

Barnefske

Sternberg

2022

Remote Sensing

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Recently, 3D point clouds have become a quasi-standard for digitization. Point cloud processing remains a challenge due to the complex and unstructured nature of point clouds. Currently, most automatic point cloud segmentation methods are data-based and gain knowledge from manually segmented ground truth (GT) point clouds. The creation of GT point clouds by capturing data with an optical sensor and then performing a manual or semi-automatic segmentation is a less studied research field. Usually, GT point clouds are semantically segmented only once and considered to be free of semantic errors. In this work, it is shown that this assumption has no overall validity if the reality is to be represented by a semantic point cloud. Our quality model has been developed to describe and evaluate semantic GT point clouds and their manual creation processes. It is applied on our dataset and publicly available point cloud datasets. Furthermore, we believe that this quality model contributes to the objective evaluation and comparability of data-based segmentation algorithms.

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Section: Ssr =mentioning

confidence: 99%

Evaluating the Quality of Semantic Segmented 3D Point Clouds

Barnefske

Sternberg

2022

Remote Sensing

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“…Currently, terrestrial LS is one of the most common methods for gathering as-built data of existing buildings (Becker et al, 2019;Dore and Murphy, 2017;Suchocki, 2020) due to the high accuracy and point density that the technique provides (Abdul Shukor et al, 2015;Alomari et al, 2016;Cheng et al, 2015;Han, 2012;Hong et al, 2015;Lee et al, 2015;Sepasgozar et al, 2015;Shanbari et al, 2016;Volk et al, 2014). A terrestrial laser scanner captures the geometry and textual information of an existing building by automatically recording millions of 3D points (Dore and Murphy, 2017;Rocha et al, 2020).…”

Section: Laser Scanningmentioning

confidence: 99%

Investigating the barriers to laser scanning implementation in building refurbishment

Uotila¹,

Saari²,

Junnonen³

2021

ITcon

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The use of the laser scanning technique has increased rapidly in the field of built environment, mainly because it produces highly accurate as-built data. However, the full potential of this technique is utilised only limitedly in building refurbishment projects. This research aims to investigate the barriers to the implementation of laser scanning in building refurbishment projects in Finland. Qualitative interviews were carried out to identify these barriers, and furthermore, to explore the current usage of the technique in building refurbishments. The study shows that many obstacles to implementations are related to challenges in laser scanning procurement, including a low level of competence in acquisition, limitations of the laser scanning technique in building refurbishments, and limitations and challenges in utilising the data in design work. This study is beneficial for building owners and practitioners as it presents the challenges and advantages that laser scanning can provide a refurbishment project. In addition, it offers suggestions to improve the early phase of a refurbishment project in order to achieve greater benefits with laser scanning. Furthermore, the findings may be utilised in the procurement process of laser scanning services in such projects, and the results may potentially solve practical challenges encountered in laser scanning work.

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“…However, in the case of older, already existing buildings, three dimensional digital models often do not exist and two dimensional plans are often faulty or outdated. Manually reconstructing digital models (as-is BIM models) for suchlike buildings is a tedious and time consuming process [72,6].…”

Section: Introductionmentioning

confidence: 99%

Pose Normalization of Indoor Mapping Datasets Partially Compliant to the Manhattan World Assumption

Hübner¹,

Weinmann²,

Wursthorn³

et al. 2021

Preprint

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Due to their potential for a variety of applications, digital building models are meanwhile well established in planning, construction and usage phases of modern building projects. Older stock buildings however frequently lack digital representations and creating these manually is a tedious and time-consuming endeavor. For this reason, as well as due to the increasing availability of mobile indoor mapping systems facilitating the straight-forward, accurate and time-efficient capture of building structures, the automated reconstruction of building models from indoor mapping data has arisen as an active field of research.In this context, many approaches rely on simplifying suppositions about the structure of buildings to be reconstructed like e.g. the well-known Manhattan World assumption. This however not only presupposes that a given building structure itself is compliant to this assumption but also that the respective indoor mapping dataset is aligned accordingly with the coordinate axes. Indoor mapping systems on the other hand typically initialize the coordinate system arbitrarily by the sensor pose at the beginning of the mapping process. Indoor mapping data thus need to be aligned with the coordinate axes as a necessary preprocessing step for many indoor reconstruction approaches, which is also frequently known as pose normalization. Even in the case of indoor reconstruction approaches that do not rely on the Manhattan World assumption, such an alignment can be beneficial as it prevents aliasing effects when using data structures like voxel grids or octrees. Furthermore, it can be useful in the context of coregistering multiple indoor mapping datasets, for the automated analysis of architectural structures as well as for stabilization and drift-reduction in indoor mapping applications.In this paper, we present a novel pose normalization method for indoor mapping point clouds and triangle meshes that is robust against large fractions of the indoor mapping geometries deviating from an ideal Manhattan World structure. In the case of building structures that contain multiple Manhattan World systems, the dominant Manhattan World structure supported by the largest fraction of geometries is determined and used for alignment. In a first step, a vertical alignment orienting a chosen axis to be orthogonal to horizontal floor and ceiling surfaces is conducted. Subsequently, a rotation around the resulting vertical axis is determined that aligns the dataset horizontally with the coordinate axes. The proposed method is evaluated quantitatively against several publicly available indoor mapping datasets. Our implementation of the proposed procedure along with code for reproducing the evaluation will be made available to the public upon acceptance for publication.

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Enabling BIM for Property Management of Existing Buildings Based on Automated As-is Capturing

Cited by 9 publications

References 0 publications

Evaluating the Quality of Semantic Segmented 3D Point Clouds

Evaluating the Quality of Semantic Segmented 3D Point Clouds

Investigating the barriers to laser scanning implementation in building refurbishment

Pose Normalization of Indoor Mapping Datasets Partially Compliant to the Manhattan World Assumption

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