Adaptive partitioning of urban facades

Shen, Chao-Hui; Huang, Shuo; Fu, Hongbo; Hu, Shi‐Min

doi:10.1145/2024156.2024218

Cited by 48 publications

(66 citation statements)

References 27 publications

(30 reference statements)

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“…To obtain the information, we designed a confidence-based floor splitting algorithm for floor analysis and extraction. Compared to other related works [15,38], our algorithm has a lower computational complexity.…”

Section: Floor Analysis and Extractionmentioning

confidence: 93%

“…This method can work well for a multi-story building but it is sensitive to image noise and irregular elements. A new floor structure splitting algorithm based on the terrestrial point cloud was presented in [38], which incorporated the above knowledge into their algorithm, and the confidence of the boundary point and the accumulated effects of the vertical or horizontal lines were evaluated for a certain splitting plane. This method is more robust and more adaptive, and it also can be applied to image data.…”

Section: Floor Analysis and Extractionmentioning

confidence: 99%

“…To evaluate the likelihood that a point lies along a boundary, we can introduce the confidence value and accumulated effect value from [38], but in [38], they used a covariance matrix to calculate the confidence value, which has a high computation complexity, to lower the computation complexity, in our algorithm, we use the method from [39] to evaluate the confidence value. In this method, four categories for the boundary point of window can be distinguished, including upper, lower, left, and right edge points.…”

Section: Floor Analysis and Extractionmentioning

confidence: 99%

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A Semantic Modelling Framework-Based Method for Building Reconstruction from Point Clouds

Zhang

et al. 2016

Remote Sensing

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Abstract:Over the past few years, there has been an increasing need for semantic information in automatic city modelling. However, due to the complexity of building structure, the semantic reconstruction of buildings is still a challenging task because it is difficult to extract architectural rules and semantic information from the data. To improve the insufficiencies, we present a semantic modelling framework-based approach for automated building reconstruction using the semantic information extracted from point clouds or images. In this approach, a semantic modelling framework is designed to describe and generate the building model, and a workflow is established for extracting the semantic information of buildings from an unorganized point cloud and converting the semantic information into the semantic modelling framework. The technical feasibility of our method is validated using three airborne laser scanning datasets, and the results are compared with other related works comprehensively, which indicate that our approach can simplify the reconstruction process from a point cloud and generate 3D building models with high accuracy and rich semantic information.

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Section: Floor Analysis and Extractionmentioning

confidence: 93%

Section: Floor Analysis and Extractionmentioning

confidence: 99%

Section: Floor Analysis and Extractionmentioning

confidence: 99%

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A Semantic Modelling Framework-Based Method for Building Reconstruction from Point Clouds

Zhang

et al. 2016

Remote Sensing

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“…Therefore an adaptive facade partition can be performed reversely to generate a hierarchical representation of the facade skin in a grid system as well as in a framework. It shows that the concept of adaptive partitioning is also applicable to flexible analyses of image facades [9]. Even an irregular lattice on facades can be inferred by combining low-level classifiers with mid-level parses by grammars.…”

Section: Shape Grammarmentioning

confidence: 97%

Parametric Models of Facade Designs of High-Rise Residential Buildings

Sung¹,

Tseng²

2016

IJET

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Abstract-High-rise residential buildings have become the most mainstream housing pattern in the world's metropolises under the current trend of urbanization. The facades of high-rise buildings are essential elements of the urban landscape. The skins of these facades are important media between the interior and exterior of high-rise buildings. They not only connect between users and environments, but also play an important functional and aesthetic role. This research involves a study of skins of high-rise residential buildings using the methodology of shape grammar to find out the rules which determine the combinations of the facade patterns and to analyze the patterns' parameters using software Grasshopper. We chose a number of facades of high-rise residential buildings as source to discover the underlying rules and concepts of the generation of facade skins.This research also provides the rules that influence the composition of facade skins. The items of the facade skins, such as windows, balconies, walls, sun visors and metal grills are treated as elements in the system of facade skins. The compositions of these elements will be categorized and described by logical rules; and the types of high-rise building facade skins will be modelled by Grasshopper. Then a variety of analyzed patterns can also be applied on other facade skins through this parametric mechanism. Using these patterns established in the models, researchers can analyze each single item to do more detailed tests and architects can apply each of these items to construct the facades for their other buildings through various combinations and permutations. The goal of these models is to develop a mechanism to generate prototypes in order to facilitate design process of various facade skins.

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“…Structure-aware reconstruction methods [9,34] on the other hand propagate sparse 2D dominant edge detections and 3D information under heavy regularization and expensive pixelwise computations. A number of restrictive assumptions have been used to simplify the problem, such as a Manhattan-world assumption (MWA) [37,9], semantic information [18], building footprints [37], hardcoded grammar rules [37] or the additional availability of dense point clouds from laser scans [33,21].…”

Section: Introductionmentioning

confidence: 99%

Patchwork Stereo: Scalable, Structure-Aware 3D Reconstruction in Man-Made Environments

Bourki

Gorce

Marlet

et al. 2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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In this paper, we address the problem of Multi-View Stereo (MVS) reconstruction of highly regular man-made scenes from calibrated, wide-baseline views and a sparse Structure-from-Motion (SfM) point cloud. We introduce a novel patch-based formulation via energy minimization which combines top-down segmentation hypotheses using appearance and vanishing line detections, as well as an arrangement of creased planar structures which are extracted automatically through a robust analysis of available SfM points and image features. The method produces a compact piecewise-planar depth map and a mesh which are aligned with the scene's structure. Experiments show that our approach not only reaches similar levels of accuracy w.r.t state-of-the-art pixel-based methods while using much fewer images, but also produces a much more compact, structure-aware mesh in a considerably shorter runtime by several of orders of magnitude.

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Adaptive partitioning of urban facades

Cited by 48 publications

References 27 publications

A Semantic Modelling Framework-Based Method for Building Reconstruction from Point Clouds

A Semantic Modelling Framework-Based Method for Building Reconstruction from Point Clouds

Parametric Models of Facade Designs of High-Rise Residential Buildings

Patchwork Stereo: Scalable, Structure-Aware 3D Reconstruction in Man-Made Environments

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