Indoor 3d Modeling and Flexible Space Subdivision From Point Clouds

Nikoohemat, S.; Diakité, Abdoulaye A.; Zlatanova, Sisi; Vosselman, G.

doi:10.5194/isprs-annals-iv-2-w5-285-2019

Cited by 18 publications

(16 citation statements)

References 29 publications

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“…Therefore, this should be considered an inconsistency. However, because the main purpose of the reconstruction of the test model was to obtain indoor spaces, and this needed a proper space closure to be extracted (Nikoohemat et al, 2019), we can afford to ignore these interaction inconsistencies in the validation process. This shows the flexibility in the validation framework and makes a link to the next step of the process, as targeted applications may make some consistency rules more relevant or critical than others, which can just be ignored.…”

Section: Resultsmentioning

confidence: 99%

“…Another important aspect when studying indoor 3D models is the data stream from which the model is created. Most of the models presented in this related work, and those which are the focus of this research, are created from point clouds and RGBD images (Furukawa, Curless, Seitz, & Szeliski, 2009; Mura et al, 2016; Nikoohemat, Diakité, Zlatanova, & Vosselman, 2019; Ochmann and Vock & Klein, 2019). However, our suggested solution can be useful also for models generated from inverse procedural modeling methods and from floor plans (Horna, Damiand, Meneveaux, & Bertrand, 2007; Okorn, Xiong, Akinci, & Huber, 2010).…”

Section: Scientific Backgroundmentioning

confidence: 99%

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Consistency grammar for 3D indoor model checking

Nikoohemat

Diakité

Lehtola

et al. 2020

Transactions in GIS

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Indoor 3D models are digital representations of building interiors reconstructed from scanned data acquired by laser scanners, digital depth (RGBD) cameras, and CAD drawings. Consequently, there is noise in the source data and a notable variety in the methods used to treat the noise and to process these data into reconstructed models. Alas, the correctness of these reconstructions and thus their suitability for a given application are uncertain. There is a lack of a robust base logic that would allow for controlling the consistency of these (automatically) generated models. Fortunately, correctness criteria are well‐defined through existing international standards. Hence, we propose a conceptual framework based on formal grammars to check the semantic, geometric, and topological consistency of a reconstructed 3D model. The proposed method proceeds in three steps to validate the model: (1) correctness checking of individual components; (2) consistency verification of instances’ interactions; and (3) model consistency check for targeted applications. Our method identifies the components in the model that violate the given rules derived from the current standards and expert knowledge. Ultimately, we propose a quantified formulation of our method that may be straightforwardly integrated into industrial‐level model checkers. The approach is independent of level of details and reconstruction method.

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Section: Resultsmentioning

confidence: 99%

Section: Scientific Backgroundmentioning

confidence: 99%

Consistency grammar for 3D indoor model checking

Nikoohemat

Diakité

Lehtola

et al. 2020

Transactions in GIS

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show abstract

“…Researchers have proposed the construction of navigation graphs based on certain semantic-based models [46,47]. Some flexible space subdivision (FSS) frameworks have been presented to account for advanced constraints during navigation or for identifying the spaces for indoor navigation [48,49]. Zlatanova et al [50] presented a framework that specifically emphasized physical and conceptual subdivisions of indoor spaces for supporting indoor localization.…”

Section: (C) Nsmmentioning

confidence: 99%

A Novel Indoor Structure Extraction Based on Dense Point Cloud

Shi

Zeng

2020

IJGI

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Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.

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“…In our workflow, the challenge was detecting the permanent changes from the dynamic changes, which were not important for the Cadastre. According to [23], this process can have an average accuracy of 93% for permanent structures and 90% for spaces [57]. Furthermore, the extraction of spaces are really crucial in the process, because the volume and area is calculated from the space subdivision result.…”

Section: Datasetmentioning

confidence: 99%

Towards 3D Indoor Cadastre Based on Change Detection from Point Clouds

et al. 2019

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3D Cadastre models capture both the complex interrelations between physical objects and their corresponding legal rights, restrictions, and responsibilities. Most of the ongoing research on 3D Cadastre worldwide is focused on interrelations at the level of buildings and infrastructures. So far, the analysis of such interrelations in terms of indoor spaces, considering the time aspect, has not been explored yet. In The Netherlands, there are many examples of changes in the functionality of buildings over time. Tracking these changes is challenging, especially when the geometry of the spaces changes as well; for example, a change in functionality, from administrative to residential use of the space or a change in the geometry when merging two spaces in a building without modifying the functionality. To record the changes, a common practice is to use 2D plans for subdivisions and assign new rights, restrictions, and responsibilities to the changed spaces in a building. In the meantime, with the advances of 3D data collection techniques, the benefits of 3D models in various forms are increasingly being researched. This work explores the opportunities for using 3D point clouds to establish a platform for 3D Cadastre studies in indoor environments. We investigate the changes in time of the geometry of the building that can be automatically detected from point clouds, and how they can be linked with a Land Administration Model (LADM) and included in a 3D spatial database, to update the 3D indoor Cadastre. The results we have obtained are promising. The permanent changes (e.g., walls, rooms) are automatically distinguished from dynamic changes (e.g., human, furniture) and are linked to the space subdivisions.

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Indoor 3d Modeling and Flexible Space Subdivision From Point Clouds

Cited by 18 publications

References 29 publications

Consistency grammar for 3D indoor model checking

Consistency grammar for 3D indoor model checking

A Novel Indoor Structure Extraction Based on Dense Point Cloud

Towards 3D Indoor Cadastre Based on Change Detection from Point Clouds

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