BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach

Tang, Shengjun; Li, Xiaoming; Zheng, Xianwei; Wu, Bo; Wang, Weixi; Zhang, Yunjie

doi:10.1016/j.autcon.2022.104422

Cited by 41 publications

(5 citation statements)

References 30 publications

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“…Furthermore, in 2007, the GSA mandated the use of BIM for spatial program validation across all of its projects. Furthermore, BIM can provide geometric and semantic information for building surfaces to aid construction inspection activities [ 17 , 46 , 47 , 48 ].…”

Section: Resultsmentioning

confidence: 99%

Content Analysis of Three-Dimensional Model Technologies and Applications for Construction: Current Trends and Future Directions

Le,

Tran,

Sturgill

2024

Sensors

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The proliferation of digital technologies is substantially transforming inspection methodologies for construction activities. Although the implementation of a three-dimensional (3D) model has emerged as an advantageous, feasible inspection application, the selection of the most suitable 3D models is challenging due to multiple technology options. The primary objectives of this study were to investigate current trends and identify future technologies for 3D models in the construction industry. This study utilized systematic reviews by identifying and selecting quality journals, analyzing selected articles, and conducting content analysis and meta-analysis to identify dominant themes in 3D models. Results showed that the top technologies used to model construction projects are building information models, remote sensing, stereo vision system/photo processing programs, and augmented reality/virtual reality. The main benefits and challenges of these technologies for modeling were also determined. This study identified three areas with significant knowledge gaps for future research: (1) the amalgamation of two or more technologies to overcome project obstacles; (2) solution optimization for inspections in remote areas; and (3) the development of algorithm-based technologies. This research contributes to the body of knowledge by exploring current trends and future directions of 3D model technologies in the construction industry.

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Section: Resultsmentioning

confidence: 99%

Content Analysis of Three-Dimensional Model Technologies and Applications for Construction: Current Trends and Future Directions

Le,

Tran,

Sturgill

2024

Sensors

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“…Wang et al [31] first used the wall line to divide the two-dimensional space, marked the dissected cell as indoor and outdoor labels, and determined walls by searching the adjacent edge of the remaining cell. Tang et al [34] used deep learning to classify indoor 3D point clouds, determined the semantics of indoor space based on morphological methods, determined the value of cells in cell complexes using classification semantics, and extracted the parameter information of wall objects to achieve the construction of a BIM model. Because the definition of wall entities surrounded by boundary surfaces was not strictly considered, the extraction of solid wall parameters was inaccurate.…”

Section: Geometry and Topology Reconstructionmentioning

confidence: 99%

Geometry and Topology Reconstruction of BIM Wall Objects from Photogrammetric Meshes and Laser Point Clouds

et al. 2023

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As the foundation for digitalization, building information modeling (BIM) technology has been widely used in the field of architecture, engineering, construction, and facility management (AEC/FM). Unmanned aerial vehicle (UAV) oblique photogrammetry and laser scanning have become increasingly popular data acquisition techniques for surveying buildings and providing original data for BIM modeling. However, the geometric and topological reconstruction of solid walls, which are among the most important architectural structures in BIM, is still a challenging undertaking. Due to noise and missing data in 3D point clouds, current research mostly focuses on segmenting wall planar surfaces from unstructured 3D point clouds and fitting the plane parameters without considering the thickness or 3D shape of the wall. Point clouds acquired only from the indoor space are insufficient for modeling exterior walls. It is also important to maintain the topological relationships between wall objects to meet the needs of complex BIM modeling. Therefore, in this study, a geometry and topology modeling method is proposed for solid walls in BIM based on photogrammetric meshes and laser point clouds. The method uses a kinetic space-partitioning algorithm to generate the building footprint and indoor floor plan. It classifies interior and exterior wall segments and infers parallel line segments to extract wall centerlines. The topological relationships are reconstructed and maintained to build wall objects with consistency. Experimental results on two datasets, including both photogrammetric meshes and indoor laser point clouds, exhibit more than 90% completeness and correctness, as well as centimeter-level accuracy of the wall surfaces.

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“…In [12], parametric building models with additional features are reconstructed from indoor scans by deriving wall candidates from vertical surfaces observed in the scans and detecting for wall openings. Combining supervised and unsupervised methods was performed by [13][14][15]. In the former, a semi-automatic approach for reconstructing heritage buildings from point clouds using a random forest classifier combined with a subpart annotated by a human was introduced.…”

Section: Related Workmentioning

confidence: 99%

Reconstructing Geometrical Models of Indoor Environments Based on Point Clouds

Kellner,

Stahl,

Reiterer

2023

Remote Sensing

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In this paper, we present a workflow that combines supervised and unsupervised methods for the reconstruction of geometric models with architectural information from unordered 3D data. Our method uses a downsampling strategy to enrich features to provide scalability for large datasets, increase robustness, and be independent of the sensor used. A Neural Network is then used to segment the resulting point cloud into basic structures. This removes furniture and clutter and preserves the relevant walls, ceilings, floors, and openings. A 2D projection combined with a graph structure is used to find a Region of Interest within the cleaned point cloud, indicating a potential room. Each detected region is projected back into a 3D data patch to refine the room candidates and allow for more complex room structures. The resulting patches are fitted with a polygon using geometric approaches. In addition, architectural features, such as windows and doors, are added to the polygon. To demonstrate that the presented approach works and that the network provides usable results, even with changing data sources, we tested the approach in different real-world scenarios with different sensor systems.

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BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach

Cited by 41 publications

References 30 publications

Content Analysis of Three-Dimensional Model Technologies and Applications for Construction: Current Trends and Future Directions

Content Analysis of Three-Dimensional Model Technologies and Applications for Construction: Current Trends and Future Directions

Geometry and Topology Reconstruction of BIM Wall Objects from Photogrammetric Meshes and Laser Point Clouds

Reconstructing Geometrical Models of Indoor Environments Based on Point Clouds

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