3D Modelling of Interior Spaces: Learning the Language of Indoor Architecture

Khoshelham, Kourosh; Díaz-Vilariño, L.

doi:10.5194/isprsarchives-xl-5-321-2014

Cited by 71 publications

(86 citation statements)

References 11 publications

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“…To accelerate data acquisition and improve the accuracy of a reconstructed model, many research groups have developed various sensor-based surveying technologies [5,9]. Laser-scanning technologies are significantly advanced [4,[10][11][12] and they can rapidly capture the details of a complex indoor structure's geometry; thus, laser-scanning technologies show promise for certain applications [8]. The high-quality reconstruction of a watertight mesh model from scanned studied [1,14], although the addressed issues are different.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

Yang

et al. 2018

Remote Sensing

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Abstract:The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing a three-dimensional (3D) indoor structure with multiple stories. With this method, the over-segmentation that usually occurs in the reconstruction of long corridors in a complex indoor environment is overcome by morphologically eroding the floor space to segment rooms and by overlapping the segmented room-space with partitioned cells via extracted wall lines. Such segmentation ensures both the integrity of the room-space partitions and the geometric regularity of the rooms. For spaces across floors in a multistory building, a peak-nadir-peak strategy in the distribution of points along the z-axis is proposed in order to extract connected areas across multiple floors. A series of experimental tests while using seven real-world 3D scans and eight synthetic models of indoor environments show the effectiveness and feasibility of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

Yang

et al. 2018

Remote Sensing

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“…In practice, most existing methods reconstruct geometric elements only (Becker et al, 2015;Díaz-Vilariño et al, 2015;Mura et al, 2016;Oesau et al, 2014;Sanchez and Zakhor, 2012;Thomson and Boehm, 2015;Valero et al, 2012;Xiao and Furukawa, 2012;Xiong et al, 2013), while a few can model spaces and the topological relations between them (Khoshelham and Díaz-Vilariño, 2014;Tran et al, 2017). Nonetheless, a comprehensive evaluation should take into account all three components of an indoor model.…”

Section: Evaluation Criteriamentioning

confidence: 99%

The Isprs Benchmark on Indoor Modelling

Khoshelham

Díaz-Vilariño

Peter

et al. 2017

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

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ABSTRACT:Automated generation of 3D indoor models from point cloud data has been a topic of intensive research in recent years. While results on various datasets have been reported in literature, a comparison of the performance of different methods has not been possible due to the lack of benchmark datasets and a common evaluation framework. The ISPRS benchmark on indoor modelling aims to address this issue by providing a public benchmark dataset and an evaluation framework for performance comparison of indoor modelling methods. In this paper, we present the benchmark dataset comprising several point clouds of indoor environments captured by different sensors. We also discuss the evaluation and comparison of indoor modelling methods based on manually created reference models and appropriate quality evaluation criteria. The benchmark dataset is available for download at:

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“…Similarly, Marson & Musse (2010) as well as Mirahmadi & Shami (2012) employ higher-level knowledge modeled as tree maps for the automatic generation of residential house layouts. Khoshelham & Díaz-Vilariño (2014) present a parametric shape grammar for modeling indoor structures using merged cuboids.…”

Section: Related Workmentioning

confidence: 99%

Grammar-Supported 3d Indoor Reconstruction From Point Clouds for “As-Built” Bim

Becker

Peter

Fritsch

2015

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

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ABSTRACT:The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data -here 3D point clouds -collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

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3D Modelling of Interior Spaces: Learning the Language of Indoor Architecture

Cited by 71 publications

References 11 publications

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

The Isprs Benchmark on Indoor Modelling

Grammar-Supported 3d Indoor Reconstruction From Point Clouds for “As-Built” Bim

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