A global optimization approach to roof segmentation from airborne lidar point clouds

Yan, Jixing; Shan, Jie; Jiang, Wanshou

doi:10.1016/j.isprsjprs.2014.04.022

Cited by 80 publications

(57 citation statements)

References 28 publications

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“…Modeling buildings has been performed by scholars adopting various processing methods that can be classified into two fundamental categories: data-driven and model-driven methods [13]. Data-driven methods utilize a LiDAR point cloud dataset for a primary data source, which utilizes several segmentation algorithms, such as region growing (RG) [14], the 3D Hough transform [15], feature clustering [16], model fitting [17] and random sample consensus (RANSAC) [18,19]. Polyhedral building models are generated from the planar segmented patches through intersection and step-edge generation and are then regulated by certain construction rules to improve the shape.…”

Section: Introductionmentioning

confidence: 99%

Semantic 3D Modeling Based on CityGML for Ancient Chinese-Style Architectural Roofs of Digital Heritage

Lin

Tang

Zhu

et al. 2017

IJGI

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Ancient Chinese-style architecture has received increased attention during the last century as a segment of cultural heritage and is of great significance, specifically in regard to the process of digitizing and modeling these buildings to preserve and protect this heritage. Because the roof form reflects the age of the structure, the structural character and the historical culture of the ancient building, constructing a refined model for the roof is a primary aspect of the 3D modeling procedure. To avoid cumbersome traditional modeling approaches that use geometry units, such as points, lines and triangles, a flexible semantic method is proposed in this study to improve modeling efficiency and reduce the professional requirements. In this method, a two-level semantic decomposition of the roof is presented according to the characteristics of ancient Chinese-style architecture. The structural level reveals the basic components that determine its structural shape, and the decorative level refers to the attached components that influence the exterior appearance. The assembly validity of the decomposed elements and the combined diversity of the integrated entities are ensured by topological constraints and derived transformations of the semantic components. This proposed method was implemented by utilizing CityGML (City Geography Markup Language) via the ADE (Application Domain Extension) mechanism and was tested by modeling the principal buildings included in the Palace Museum.

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Section: Introductionmentioning

confidence: 99%

Semantic 3D Modeling Based on CityGML for Ancient Chinese-Style Architectural Roofs of Digital Heritage

Lin

Tang

Zhu

et al. 2017

IJGI

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“…The computation of the normal vectors becomes unstable when noise points exist or the supporting points are not properly selected. In addition, these methods may lead to over-segmentation or under-segmentation in the surface intersection region and noisy areas [38].…”

Section: Introductionmentioning

confidence: 99%

“…The segmentation can be modeled as an optimization problem to determine the best graph cut [39][40][41]. The frequently used graph cut algorithms are minimum spanning tree [42], normalized cut [43,44] and Graphcuts [38]. Other optimization methods, such as level set, are also utilized to segment planes [45].…”

Section: Introductionmentioning

confidence: 99%

“…Other optimization methods, such as level set, are also utilized to segment planes [45]. A recent study has shown that using Graphcuts to optimize the initial segmentation [38] significantly improves the initial over-segmentation and eliminates the cross-planes. The limitation of this method is that the result relies on the initial segmentation, and the speed is low because of its iterative optimization operation [46].…”

Section: Introductionmentioning

confidence: 99%

“…The final facets can be obtained after selecting the seed CLEs and combining CLE growth and point growth. Comparison of CLEG with other popular methods, such as RANSAC [19], 3D Hough transformation [27], PCA [36], iterative PCA [37] and a state-of-the-art global optimization-based algorithm [38], shows that the proposed algorithm runs much faster than them and produces stable and accurate results. The remainder of the paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

2016

Remote Sensing

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Plane segmentation is an important step in feature extraction and 3D modeling from light detection and ranging (LiDAR) point cloud. The accuracy and speed of plane segmentation are two issues difficult to balance, particularly when dealing with a massive point cloud with millions of points. A fast and easy-to-implement algorithm of plane segmentation based on cross-line element growth (CLEG) is proposed in this study. The point cloud is converted into grid data. The points are segmented into line segments with the Douglas-Peucker algorithm. Each point is then assigned to a cross-line element (CLE) obtained by segmenting the points in the cross-directions. A CLE determines one plane, and this is the rationale of the algorithm. CLE growth and point growth are combined after selecting the seed CLE to obtain the segmented facets. The CLEG algorithm is validated by comparing it with popular methods, such as RANSAC, 3D Hough transformation, principal component analysis (PCA), iterative PCA, and a state-of-the-art global optimization-based algorithm. Experiments indicate that the CLEG algorithm runs much faster than the other algorithms. The method can produce accurate segmentation at a speed of 6 s per 3 million points. The proposed method also exhibits good accuracy.

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Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method

Walczak

Andrzejczak

Scherer

et al. 2020

Lecture Notes in Computer Science

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A global optimization approach to roof segmentation from airborne lidar point clouds

Cited by 80 publications

References 28 publications

Semantic 3D Modeling Based on CityGML for Ancient Chinese-Style Architectural Roofs of Digital Heritage

Semantic 3D Modeling Based on CityGML for Ancient Chinese-Style Architectural Roofs of Digital Heritage

Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method

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