Productive modeling for development of as-built BIM of existing indoor structures

Jung, Jae Hwan; Hong, Sungchul; Jeong, Sang Seom; Kim, Sang-Min; Cho, Hyoungsig; Hong, Seunghwan; Heo, Joon

doi:10.1016/j.autcon.2014.02.021

Cited by 206 publications

(130 citation statements)

References 26 publications

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“…Results of this automatic object recognition approach include a polygon model for detected building features. Much of the existing work for automated object recognition and feature detection show promising results but do not automate the complete process from point cloud to BIM (Hong et al, 2015, Jung et al, 2014, Xiong et al, 2013, Zhang and Zakhor, 2014, Wang et al, 2015. The results from these automatic approaches include surface models, planes, 3D vectors or a subset of the original point cloud, all of which still need to be converted into structured information enhanced and parametric BIM components which at present needs to be carried out manually (Volk et al, 2014, Thomson andBoehm, 2015).…”

Section: Automation For As-built Bimmentioning

confidence: 99%

“…Once objects are automatically recognised from a dataset, primitives or planes can be automatically fitted to the recognised elements in the dataset. A lot of the research in this area, however, is focused on indoor applications (Jung et al, 2014, Zhang and Zakhor, 2014, Thomson and Boehm, 2015 and is currently restricted to automatic extraction of basic elements such as planes and openings. Automatic extraction of complex architectural elements that occur in existing and historical buildings is still in its infancy.…”

Section: Automation For As-built Bimmentioning

confidence: 99%

“…One area where progress is being made towards automated as-built BIM is in automated object recognition and feature extraction from point clouds (Jung et al, 2014, Xiong et al, 2013, Zhang and Zakhor, 2014. Object recognition is the problem of automatically labelling data points or segments of an image with a named object or object class (Tang et al, 2010), for example automatically detecting windows or doors from an image or point cloud.…”

Section: Automation For As-built Bimmentioning

confidence: 99%

See 2 more Smart Citations

Current State of the Art Historic Building Information Modelling

Dore¹,

Murphy²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

114

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ABSTRACT:In an extensive review of existing literature a number of observations were made in relation to the current approaches for recording and modelling existing buildings and environments: Data collection and pre-processing techniques are becoming increasingly automated to allow for near real-time data capture and fast processing of this data for later modelling applications. Current BIM software is almost completely focused on new buildings and has very limited tools and pre-defined libraries for modelling existing and historic buildings. The development of reusable parametric library objects for existing and historic buildings supports modelling with high levels of detail while decreasing the modelling time. Mapping these parametric objects to survey data, however, is still a time-consuming task that requires further research. Promising developments have been made towards automatic object recognition and feature extraction from point clouds for as-built BIM. However, results are currently limited to simple and planar features. Further work is required for automatic accurate and reliable reconstruction of complex geometries from point cloud data. Procedural modelling can provide an automated solution for generating 3D geometries but lacks the detail and accuracy required for most asbuilt applications in AEC and heritage fields.

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Section: Automation For As-built Bimmentioning

confidence: 99%

Section: Automation For As-built Bimmentioning

confidence: 99%

Section: Automation For As-built Bimmentioning

confidence: 99%

See 1 more Smart Citation

Current State of the Art Historic Building Information Modelling

Dore¹,

Murphy²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

114

View full text Add to dashboard Cite

show abstract

“…Furthermore, end sides are normally not visible in roof constructions, as they occur in the beam joints. Jung et al (2014) use a similar approach, but suggest to concentrate on a semi-automatic method, in which outlines of planar faces are determined automatically, and are used for subsequent manual modeling for the as-built BIM (Building Information Modeling). Volk et al (2014) discusses as-built BIM and Murphy et al (2013) especially historic BIM (HBIM).…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

Automated Reconstruction of Historic Roof Structures From Point Clouds – Development and Examples

Pöchtrager

Styhler-Aydın

Döring-Williams

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The analysis of historic roof constructions is an important task for planning the adaptive reuse of buildings or for maintenance and restoration issues. Current approaches to modeling roof constructions consist of several consecutive operations that need to be done manually or using semi-automatic routines. To increase efficiency and allow the focus to be on analysis rather than on data processing, a set of methods was developed for the fully automated analysis of the roof constructions, including integration of architectural and structural modeling. Terrestrial laser scanning permits high-detail surveying of large-scale structures within a short time. Whereas 3-D laser scan data consist of millions of single points on the object surface, we need a geometric description of structural elements in order to obtain a structural model consisting of beam axis and connections. Preliminary results showed that the developed methods work well for beams in flawless condition with a quadratic cross section and no bending. Deformations or damages such as cracks and cuts on the wooden beams can lead to incomplete representations in the model. Overall, a high degree of automation was achieved.

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“…In this process, the algorithm defines which pixels are connected to other pixels based on the number of connected neighborhoods (4-connectivity was assumed in the present study). Each connected segment is uniquely labeled with different integer values, and its pixels are counted [12,36]. Among the labeled segments, the largest one is retained; the others, considered to be noise, are removed.…”

Section: Rasterization and Noise Filteringmentioning

confidence: 99%

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Jung

Stachniss

Kim

2017

IJGI

Self Cite

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Abstract:In this paper, we introduce an automatic room segmentation approach based on morphological processing. The inputs are registered point-clouds obtained from either a static laser scanner or a mobile scanning system, without any required prior information or initial labeling satisfying specific conditions. The proposed segmentation method's main concept, based on the assumption that each room is bound by vertical walls, is to project the 3D point cloud onto a 2D binary map and to close all openings (e.g., doorways) to other rooms. This is achieved by creating an initial segment map, skeletonizing the surrounding walls of each segment, and iteratively connecting the closest pixels between the skeletonized walls. By iterating this procedure for all initial segments, the algorithm produces a "watertight" floor map, on which each room can be segmented by a labeling process. Finally, the original 3D points are segmented according to their 2D locations as projected on the segment map. The novel features of our approach are: (1) its robustness against occlusions and clutter in point-cloud input; (2) high segmentation performance regardless of the number of rooms or architectural complexity; and (3) straight segmentation boundary generation, all of which were proved in experiments with various sets of real-world, synthetic, and publicly available data. Additionally, comparisons with the five popular existing methods through both qualitative and quantitative evaluations demonstrated the feasibility of the proposed approach.

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Productive modeling for development of as-built BIM of existing indoor structures

Cited by 206 publications

References 26 publications

Current State of the Art Historic Building Information Modelling

Current State of the Art Historic Building Information Modelling

Automated Reconstruction of Historic Roof Structures From Point Clouds – Development and Examples

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

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