Detecting and inferring repetitive elements with accurate locations and shapes from façades

Lian, Yongjian; Shen, Xukun; Hu, Yong

doi:10.1007/s00371-017-1355-z

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…Mathias et al [17] proposed a three-layered system for semantic segmentation of building facades, in which three levels of abstraction are represented in facade images: segments, objects and architectural elements. Lian et al [18] employed the color clustering method to automatically derive candidate templates. Then, an adaptive region descriptor and a Bayesian network were used for repetition detection and occlusion inference.…”

Section: A Image-based Methodsmentioning

confidence: 99%

Saliency-Guided Repetition Detection From Facade Point Clouds

et al. 2019

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We present a saliency-guided algorithm for detecting the locations of repetitive structures on building facades. First, the global and local saliencies of each point are determined by measuring the global rarity and the local distinctness. The saliency map is utilized to adaptively extract the salient points. Second, the salient points are vertically sliced. A curve can be derived by counting the total number of points in each slice. Then, the curve is converted into a square wave to locate the vertical splitting position. Next, each segment is horizontally sliced, similar to the vertical splitting. The salient points are partitioned into repetitive candidates after the vertical and horizontal splitting. Finally, the repetitive candidates are refined according to the similarity of the neighborhood and the regularity of the arrangement. The experimental results demonstrate that our method can quickly and effectively extract repetitions from facade point clouds.

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Section: A Image-based Methodsmentioning

confidence: 99%

Saliency-Guided Repetition Detection From Facade Point Clouds

et al. 2019

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“…However, in this case similarities in the model are detected by considering a local similarity measure of the point cloud curvature which is more prone to output outliers. Lian et al (2018) present an algorithm for automatically detecting and inferring repetitive elements from façades. Firstly, candidate templates are automatically derived starting on the clustering of similar features.…”

Section: Related Workmentioning

confidence: 99%

Automated Detection and Layout Regularization of Similar Features in Indoor Point Cloud

Previtali¹,

Barazzetti²,

Roncoroni³

2019

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

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<p><strong>Abstract.</strong> Automated identification of high-level structures in unorganized point cloud of indoor spaces Indoor space is an important aspect of scene analysis that provides essential information for many applications, such as building digitization, indoor navigation and evacuation route planning. In addition, detection of repetition and regularities in the organization indoor environments, such as rooms, can be used to provide a contextual relationship in the reconstruction phase. However, retrieving high-level information is a challenging task due to the unorganized nature of the raw data, poor-quality of the input data that are in many cases contaminated with noise and outliers. in point benefit from the apparent regularities and strong contextual relationships in façades. The main observation exploited in this paper is the fact that building indoor is generally constituted by a set of basic shapes repeated several times in regular layouts. Building elements can be considered as similar if they share a set of features and elements in an idealized layout exhibiting some regularities. Starting from this main assumption a recursive adaptive partitioning of the indoor point cloud is carried out to automatically derive a flexible and hierarchical 3D representation of the building space. The presented methodology is tested on a synthetic dataset with Gaussian noise. The reconstructed pattern shows a close correspondence with the synthetic one showing the viability of the proposed approach.</p>

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Detecting and inferring repetitive elements with accurate locations and shapes from façades

Cited by 2 publications

References 45 publications

Saliency-Guided Repetition Detection From Facade Point Clouds

Saliency-Guided Repetition Detection From Facade Point Clouds

Automated Detection and Layout Regularization of Similar Features in Indoor Point Cloud

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