Shape Grammar Approach to 3D Modeling of Indoor Environments Using Point Clouds

Tran, Hau; Khoshelham, Kourosh; Kealy, Allison; Díaz-Vilariño, L.

doi:10.1061/(asce)cp.1943-5487.0000800

Cited by 65 publications

(76 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The geometric quality measures were computed for a set of 3D models reconstructed automatically from the benchmark dataset using the shape grammar approach described in (Khoshelham and Díaz-Vilariño, 2014) and (Tran et al, 2018). Figure 6 shows the reconstructed source models and the corresponding reference models in which each surface is marked as either interpreted (dark grey) or observed (light grey and yellow).…”

Section: Methodsmentioning

confidence: 99%

“…Quantitative measures derived from a comparison of the model with the data, e.g. a point cloud, have been used in several other works (Macher et al, 2017;Tran et al, 2018;Valero et al, 2012). Comparison with ground truth or a reference model has also been used in a few works for quantitative evaluation of automatically generated indoor models (Díaz-Vilariño et al, 2015;Oesau et al, 2014;Thomson and Boehm, 2015;Xiong et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Evaluation Framework for Benchmarking Indoor Modelling Methods

Khoshelham

Tran

Díaz-Vilariño

et al. 2018

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

Self Cite

View full text Add to dashboard Cite

Abstract. Despite recent progress in the development of methods for automated reconstruction of indoor models, a comparative performance evaluation of these methods is not available due to the lack of publicly available benchmark datasets and a common evaluation framework. The ISPRS Benchmark on Indoor Modelling is an effort to enable comparison and benchmarking of indoor modelling methods by providing a benchmark dataset and a comprehensive evaluation framework. In this paper, we propose a framework for the evaluation of indoor modelling methods, and discuss various quality aspects of the reconstruction methods as well as the reconstructed models. We discuss the challenges in quantitative quality evaluation of indoor models through comparison with a reference model, and propose suitable measures and methods for comparing an automatically reconstructed indoor model with a reference.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evaluation Framework for Benchmarking Indoor Modelling Methods

Khoshelham

Tran

Díaz-Vilariño

et al. 2018

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, there has been a progress towards reconstruction of indoor models from point clouds, although most of these works are limited to clutter-free environments or Manhattan-World structures (Becker et al, 2015;Khoshelham and Diaz-Vilarino, 2014). Other works reduce the complexity of the structure assuming walls are always vertical or ceilings are at the same height which generates a 2.5D model (Ikehata et al, 2015;Tran H. et al, 2019;Turner et al, 2015). Few works deal with arbitrary wall layout and slanted walls (Li et al, 2018;Mura et al, 2016;Nikoohemat et al, 2018).…”

Section: Indoor 3d Modelling From Point Cloudsmentioning

confidence: 99%

Indoor 3d Modeling and Flexible Space Subdivision From Point Clouds

Nikoohemat

Diakité

Zlatanova

et al. 2019

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

View full text Add to dashboard Cite

Abstract. Indoor navigation can be a tedious process in a complex and unknown environment. It gets more critical when the first responders try to intervene in a big building after a disaster has occurred. For such cases, an accurate map of the building is among the best supports possible. Unfortunately, such a map is not always available, or generally outdated and imprecise, leading to error prone decisions. Thanks to advances in the laser scanning, accurate 3D maps can be built in relatively small amount of time using all sort of laser scanners (stationary, mobile, drone), although the information they provide is generally an unstructured point cloud. While most of the existing approaches try to extensively process the point cloud in order to produce an accurate architectural model of the scanned building, similar to a Building Information Model (BIM), we have adopted a space-focused approach. This paper presents our framework that starts from point-clouds of complex indoor environments, performs advanced processes to identify the 3D structures critical to navigation and path planning, and provides fine-grained navigation networks that account for obstacles and spatial accessibility of the navigating agents. The method involves generating a volumetric-wall vector model from the point cloud, identifying the obstacles and extracting the navigable 3D spaces. Our work contributes a new approach for space subdivision without the need of using laser scanner positions or viewpoints. Unlike 2D cell decomposition or a binary space partitioning, this work introduces a space enclosure method to deal with 3D space extraction and non-Manhattan World architecture. The results show more than 90% of spaces are correctly extracted. The approach is tested on several real buildings and relies on the latest advances in indoor navigation.

show abstract

“…Often, these approaches extract 3D geometries of buildings based purely on a data-driven process, which heavily depend on the quality of the data [6][7][8]. Alternatively, a few works initially adopt shape grammars, exploiting structural arrangement and architectural design principles, in the indoor reconstruction [9][10][11]. Within the field of urban reconstruction, shape grammars are widely and quite successfully used for 3D synthesizing of architecture (e.g., building façades) [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Procedural Reconstruction of 3D Indoor Models from Lidar Data Using Reversible Jump Markov Chain Monte Carlo

Tran

Khoshelham

2020

Remote Sensing

View full text Add to dashboard Cite

Automated reconstruction of Building Information Models (BIMs) from point clouds has been an intensive and challenging research topic for decades. Traditionally, 3D models of indoor environments are reconstructed purely by data-driven methods, which are susceptible to erroneous and incomplete data. Procedural-based methods such as the shape grammar are more robust to uncertainty and incompleteness of the data as they exploit the regularity and repetition of structural elements and architectural design principles in the reconstruction. Nevertheless, these methods are often limited to simple architectural styles: the so-called Manhattan design. In this paper, we propose a new method based on a combination of a shape grammar and a data-driven process for procedural modelling of indoor environments from a point cloud. The core idea behind the integration is to apply a stochastic process based on reversible jump Markov Chain Monte Carlo (rjMCMC) to guide the automated application of grammar rules in the derivation of a 3D indoor model. Experiments on synthetic and real data sets show the applicability of the method to efficiently generate 3D indoor models of both Manhattan and non-Manhattan environments with high accuracy, completeness, and correctness.

show abstract

Shape Grammar Approach to 3D Modeling of Indoor Environments Using Point Clouds

Cited by 65 publications

References 37 publications

An Evaluation Framework for Benchmarking Indoor Modelling Methods

An Evaluation Framework for Benchmarking Indoor Modelling Methods

Indoor 3d Modeling and Flexible Space Subdivision From Point Clouds

Procedural Reconstruction of 3D Indoor Models from Lidar Data Using Reversible Jump Markov Chain Monte Carlo

Contact Info

Product

Resources

About