Semantic scan planning for indoor structural elements of buildings

Quintana, Blanca Hernández; Prieto, Samuel A.; Adn, A.; Vzquez, A.S.

doi:10.1016/j.aei.2016.08.003

Cited by 34 publications

(21 citation statements)

References 24 publications

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“…Their approach reconstructs walls as entities with constraints on other entities, retaining wall relationships and deprecating the modification of one element onto the other. The authors of [23,[102][103][104] extend the processes of the parametric modelling of walls and openings to BIM modelling applications. More recently, the paper by Macher et al [68] presented a semi-automatic approach for the 3D reconstruction of walls, slabs and openings from a point cloud of a multi-storey building, and provides a proof of concept of OBJ for IFC manual creation.…”

Section: Knowledge Integration (Ki) For Object-relationship Modellingmentioning

confidence: 99%

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

et al. 2018

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3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework that leverages segmented point cloud data and domain ontologies. Our approach follows a part-to-whole conception which models a point cloud in parametric elements usable per instance and aggregated to obtain a global 3D model. We first extract analytic features, object relationships and contextual information to permit better object characterization. Then, we propose a multi-representation modelling mechanism augmented by automatic recognition and fitting from the 3D library ModelNet10 to provide the best candidates for several 3D scans of furniture. Finally, we combine every element to obtain a consistent indoor hybrid 3D model. The method allows a wide range of applications from interior navigation to virtual stores.

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Section: Knowledge Integration (Ki) For Object-relationship Modellingmentioning

confidence: 99%

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

et al. 2018

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“…Future developments of these systems continues to design a compact, lightweight and relatively inexpensive multipurpose laser scanning system that should be quick and easy to deploy, and requires a minimum amount of existing equipment for operational support [1]. The well-known features of laser scanning data are high accuracy and density and as a result of which big spatial point cloud data are created [15]. The process of obtaining point cloud for each type of laser scanning is well explored and the appropriate software have been developed by providers [16].…”

Section: Discussionmentioning

confidence: 99%

Hybrid processing of laser scanning data

2018

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Abstract. In this article the analysis of gaps in processing of raw laser scanning data and results of bridging the gaps discovered on the base of usage of laser scanning data for historic building information modeling is presented. The results of the development of a unified hybrid technology for the processing, storage, access and visualization of combined laser scanning and photography data about historical buildings are analyzed. The first result of the technology application for the historical building of St. Petersburg Polytechnic University shows reliability of the proposed approaches.

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“…Next, the voxel space is projected to a 2D image with labelled pixels, and the next scanning position corresponds to the empty pixel from which the largest number of occluded pixels are observable. Quintana et al [28] extend the previous work in a way that the NBV problem is devoted to completing the acquisition of building structural elements, and consequently, floors, walls, columns and ceilings are recognized in successive scans. Prieto el al.…”

Section: Scan-planning In Indoor Environmentsmentioning

confidence: 94%

From BIM to Scan Planning and Optimization for Construction Control

et al. 2019

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Scan planning of buildings under construction is a key issue for an efficient assessment of work progress. This work presents an automatic method aimed to determinate the optimal scan positions and the optimal route based on the use of Building Information Models (BIM) and considering data completeness as stopping criteria. The method is considered for a Terrestrial Laser Scanner mounted on a mobile robot following a stop & go procedure. The method starts by extracting floor plans from the BIM model according to the planned construction status, and including geometry and semantics of the building elements considered for construction control. The navigable space is defined from a binary map considering a security distance to building elements. After a grid-based and a triangulation-based distribution are implemented for generating scan position candidates, a visibility analysis is carried out to determine the optimal number and position of scans. The optimal route to visit all scan positions is addressed by using a probabilistic ant colony optimization algorithm. The method has been tested in simulated and real buildings under very dissimilar conditions and structural construction elements. The two approaches for generating scan position candidates are evaluated and results show the triangulation-based distribution as the more efficient approach in terms of processing and acquisition time, especially for large-scale buildings.

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Semantic scan planning for indoor structural elements of buildings

Cited by 34 publications

References 24 publications

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

Hybrid processing of laser scanning data

From BIM to Scan Planning and Optimization for Construction Control

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