Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

Wang, Yongzhi; Ma, Yuqing; Zhu, A‐Xing; Zhao, Hui; Liao, Lixia

doi:10.1016/j.isprsjprs.2017.11.015

Cited by 34 publications

(29 citation statements)

References 26 publications

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“…In recent years, methods and strategies for retrieving as-built information from PC_Sets utilizing image processing and mathematical procedures show an important progress in the field, since efforts focus on retrieving detailed information useful for creating as-built BIM models suitable for building retrofitting, Facilities Management (FM), Heritage management (HM), and Asset Management (AM) [10,[13][14][15]. As some works focus on item recognition from images [16], which still have to overcome accuracy issues [17], others concentrate on solving more specific problems such as complex spaces [18,19] or facade arrangements [4]. Other than the focus, challenges arise when recognizing elements such as doors or windows, processing facade patterns, or when resolving contour distortions caused by occluding objects and/or topologic variations.…”

Section: Related Workmentioning

confidence: 99%

Parametric point-cloud slicing for facade retrofitting

Bohórquez

Derigent

El-Haouzi

2021

International Journal of Architectural Computing

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This work presents a method for retrieving 3D building contours usable in facade retrofitting projects, which uses a parametric modeling workflow that utilizes a point-cloud slicing method to retrieve such 3D contours. Since current commitments by European governments seek to reduce energy consumption as a means to reduce carbon emissions from building stock by 2050, facade retrofitting appears as an alternative for addressing operational and embedded building emissions. Within such a context, the main contribution of this work consists of a workflow and a 3D reconstruction solution that uses a parametric environment for capturing building topology and bypassing ground-level occlusions. A real case study and a strategy for converting 3D building contours into Industry Foundation Classes entities, directly from the parametric modeling environment, served as a scenario for testing the capabilities of a Grasshopper solution and open new perspectives for this approach.

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Section: Related Workmentioning

confidence: 99%

Parametric point-cloud slicing for facade retrofitting

Bohórquez

Derigent

El-Haouzi

2021

International Journal of Architectural Computing

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“…In Formula (1), I p represents the input image, p represents the indices of 2D image pixels, and S p represents an output structural image with fused texture. (∂ x S) p and (∂ y S) p are the partial derivatives in the x and y directions for pixel p. RTV contains general pixel-wise windowed TV measures, shown as Formulas (2). Furthermore, RTV can distinguish prominent structural features from the textural elements [34].…”

Section: B Texture Fusion Of Buiding Facade Imagesmentioning

confidence: 99%

“…The RANSAC algorithm is extensively used to detect geometric elements, such as lines and planes [40]. Building facades contain many geometric features, such as edges of windows and gates [2]. Therefore, using RANSAC is feasible to optimize the initial facade features.…”

Section: Structural Feature Extraction Of Building Facadesmentioning

confidence: 99%

Accurate Extraction Method for Structural Features of Building Facades Through Texture Fusion

Wang

2020

IEEE Access

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Facade structural features can represent the overall framework of buildings. However, structural features extracted by the current methods contain a quantity of trivial unstructured information. In this study, we proposed an accurate extraction method for structural features of building facades through texture fusion. By performing texture fusion on building facade images, the interference of textural elements on structural feature extraction could be eliminated. After texture fusion, the line segment detector (LSD) algorithm is used to extract the structural features from the building facade images, and random sample consensus (RANSAC) is used to improve the continuity of structural features. The accuracy and effectiveness of the proposed method was demonstrated by comparing results with the state-of-the-art methods, such as LSD, MLSD, CannyLines, and MCMLSD. Value setting of three important parameters is discussed in detail. The imagery facade features extracted through the proposed method provide valuable support for many fields, such as image feature registration and 3D reconstruction of building surfaces.

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“…For MLS point clouds, some people [ 5 , 6 ] tend to extract building based on the spatial distribution patterns, however, the performance of their results was restricted by the quality of data. Wang et al [ 7 ] proposed an efficient method to highly extract building facade by combining the point clouds and optical images. Pu et al [ 8 ] proposed a building facade recognition algorithm based on knowledge rules, but it has certain limitations and is difficult to apply to complex outdoor scenes.…”

Section: Introductionmentioning

confidence: 99%

Semantic-Based Building Extraction from LiDAR Point Clouds Using Contexts and Optimization in Complex Environment

Wang

Jiang

et al. 2020

Sensors

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The extraction of buildings has been an essential part of the field of LiDAR point clouds processing in recent years. However, it is still challenging to extract buildings from huge amount of point clouds due to the complicated and incomplete structures, occlusions and local similarities between different categories in a complex environment. Taking the urban and campus scene as examples, this paper presents a versatile and hierarchical semantic-based method for building extraction using LiDAR point clouds. The proposed method first performs a series of preprocessing operations, such as removing ground points, establishing super-points and using them as primitives for subsequent processing, and then semantically labels the raw LiDAR data. In the feature engineering process, considering the purpose of this article is to extract buildings, we tend to choose the features extracted from super-points that can describe building for the next classification. There are a portion of inaccurate labeling results due to incomplete or overly complex scenes, a Markov Random Field (MRF) optimization model is constructed for postprocessing and segmentation results refinement. Finally, the buildings are extracted from the labeled points. Experimental verification was performed on three datasets in different scenes, our results were compared with the state-of-the-art methods. These evaluation results demonstrate the feasibility and effectiveness of the proposed method for extracting buildings from LiDAR point clouds in multiple environments.

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Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

Cited by 34 publications

References 26 publications

Parametric point-cloud slicing for facade retrofitting

Parametric point-cloud slicing for facade retrofitting

Accurate Extraction Method for Structural Features of Building Facades Through Texture Fusion

Semantic-Based Building Extraction from LiDAR Point Clouds Using Contexts and Optimization in Complex Environment

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