Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles

Xu, Yangsheng; Yao, Wei; Tuttas, Sebastian; Hoegner, L.; Stilla, Uwe

doi:10.1109/jstars.2018.2817227

Cited by 42 publications

(31 citation statements)

References 59 publications

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“…Similar to RG, feature clustering is also a process of grouping points with similar features into a cluster under common constraints. K-means (Shi et al, 2011), fuzzy C-means (Biosca and Lerma, 2008), hierarchical clustering (Xu et al, 2018) are frequently adopted for segmentation task of the point clouds. Compared with RG, one advantage of such approaches is that no seeds are needed for initialization.…”

Section: Point Cloud Segmentationmentioning

confidence: 99%

Unsupervised Window Extraction From Photogrammetric Point Clouds With Thermal Attributes

Lin

Dong

Zhang

et al. 2019

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

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Abstract. The automatic extraction of windows from photogrammetric data has achieved increasing attention in recent times. An unsupervised windows extraction approach from photogrammetric point clouds with thermal attributes is proposed in this study. First, point cloud segmentation is conducted by a popular workflow: Multiscale supervoxel generation is applied to the image-based 3D point cloud, followed by region growing and energy optimization using spatial positions and thermal attributes of the raw points. Afterwards, an object-based feature (window index) is extracted using the average thermal attribute and the size of the object. Next, thresholding is applied to extract initial window regions. Finally, several criterions are applied to further refine the extraction results. For practical validation, the approach is evaluated on an art nouveau building row façade located at Dresden, Germany.

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Section: Point Cloud Segmentationmentioning

confidence: 99%

Unsupervised Window Extraction From Photogrammetric Point Clouds With Thermal Attributes

Lin

Dong

Zhang

et al. 2019

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

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“…The MLS dataset is also used for a brief evaluation of segmentation performance. Manual segmentation from (Xu et al, 2018c) is used as references (see Figs. 5d-5g) in a way similar to the work in (Vo et al, 2015).…”

Section: Testing Datasetsmentioning

confidence: 99%

“…We first test our proposed global graph-based clustering method on the reference datasets, which has been mentioned and used in (Xu et al, 2018c). The voxel sizes used in our is set to 0.1m, and seed resolutions of supervoxels in our proposed method, Locally Convex Connected Patches (LCCP), and Supervoxel and graph-based segmentation (SVGS) are both set to 0.25 m.…”

Section: Point Cloud Segmentationmentioning

confidence: 99%

Contour Extraction of Planar Elements of Building Facades From Point Clouds Using Global Graph-Based Clustering

Stilla

2019

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

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Abstract. In this work, we present a surface-based method to extract the contours of planar building elements in the urban scene. A bottom-up segmentation method that utilizes global graph-based optimization and supervoxel structure is developed, enabling an automatic and unsupervised segmentation of point clouds. Then, a planarity-based extraction is conducted to segments, and only the planar segments, as well as their neighborhoods, are selected as candidates for the plane fitting. The points of the plane can be identified by the parametric model given by the planarity calculation. Afterward, the boundary points of the extracted plane are extracted by the alpha-shape. Optionally, line segments can be fitted and optimized by the energy minimization with the local graphical model. The experimental results using different datasets reveal that our proposed segmentation methods can be effective and comparable with other method, and the contours of planar building elements can be well extracted from the complex urban scene.

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“…Clustering method: This method is an unsupervised learning method, which heuristically clusters points with similar attributes into the same class to meet the requirements of cost functions. Representative methods include the classic K-means algorithm [23], Euclidean distance clustering algorithm [24], mean shift clustering [25][26][27], hierarchical clustering [28][29][30], sample density-based clustering [31,32], and mixed kernel density function clustering [33]. For example, Wu et al [24] introduced a smooth threshold constraint to the traditional Euclidean clustering algorithm to prevent over-and/or under-segmentation problems.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Chen

et al. 2019

Remote Sensing

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This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In the hierarchical clustering, the raw point clouds are over-segmented into a set of fine-grained clusters by integrating the point density clustering and the classic K-means clustering algorithm, followed by the proposed probability density clustering algorithm. Through this process, we not only obtain a more uniform size and more homogeneous clusters with semantic consistency, but the topological relationships of the cluster’s neighborhood are implicitly maintained by turning the problem of topology maintenance into a clustering problem based on the proposed probability density clustering algorithm. Subsequently, the fine-grained clusters and their topological context are fed into the CRF labeling step, from which the fine-grained cluster’s semantic labels are learned and determined by solving a multi-label energy minimization formulation, which simultaneously considers the unary, pairwise, and higher-order potentials. Our experiments of classifying urban and residential scenes demonstrate that the proposed approach reaches 88.5% and 86.1% of “m F 1 ” estimated by averaging all classes of the F 1 -scores. We prove that the proposed method outperforms five other state-of-the-art methods. In addition, we demonstrate the effectiveness of the proposed energy terms by using an “ablation study” strategy.

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Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles

Cited by 42 publications

References 59 publications

Unsupervised Window Extraction From Photogrammetric Point Clouds With Thermal Attributes

Unsupervised Window Extraction From Photogrammetric Point Clouds With Thermal Attributes

Contour Extraction of Planar Elements of Building Facades From Point Clouds Using Global Graph-Based Clustering

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

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