A Sliding Window Method for Detecting Corners of Openings From Terrestrial Lidar Data

Li, J.; Xiong, Boli; Biljecki, Filip; Schrotter, Gerhard

doi:10.5194/isprs-archives-xlii-4-w10-97-2018

Cited by 8 publications

(8 citation statements)

References 27 publications

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“…Rules play an important role in detailed façade reconstruction as openings follow architectural rules concerning their shape and spatial relationships (e.g., the locations of windows and doors relative to walls). Rules can be applied to openings extracted from corner detectors [9,107], a graph model [73], machine learning [108] or based on the assumption of symmetry in window distribution [109].…”

Section: Scene Parsingmentioning

confidence: 99%

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

et al. 2022

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Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research.

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Section: Scene Parsingmentioning

confidence: 99%

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

et al. 2022

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“…Mesolongitis and Stamos [29] propose a voting scheme to detect windows with the assumption that they are arranged in periodic structures. Li et al [8] use a sliding window method to analyze the facade structural regularity for detecting corners of openings. Similarly, Nan et al [30] detect the repetition of a facade from images that guides an assembling of 3D templates and a coarse model for a detailed building model reconstruction.…”

Section: Rule and Structure Based Reconstructionmentioning

confidence: 99%

“…Recently, under the Virtual Singapore project, the efficient facade modelling was investigated as one of the focal points [6,7]. The integration of openings improves reality-based visualization and simulation through augmented reality (AR) or virtual reality (VR) for decision making, but also analyses of building energy performance [8,9] and many other applications. A reliable reconstruction of openings offers a higher level of reality in digital models and superior experience to users and provides convincing data for 3D analyses, i.e., glazing ratio determination, wayfinding, 5G signal simulation [10], etc.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Inference Machine for Global Alignment of Wall Openings

Li¹,

Xiong

Qin

et al. 2020

Remote Sensing

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Openings such as windows and doors are essential components of architectural wall surfaces. It is still a challenge to reconstruct them robustly from unstructured 3D point clouds because of occlusions, noises and non-uniformly distributed points. Current research primarily focuses on meliorating the robustness of detection and pays little attention to the geometric correctness. To improve the reconstruction quality, assumptions on the opening layout are usually applied as rules to support the reconstruction algorithm. The commonly used assumptions, such as the strict grid and symmetry pattern, however, are not suitable in many cases. In this paper, we propose a novel approach, named an inference machine, to identify and use flexible rules in wall opening modelling. Our method first detects and models openings through a data-driven method and then refines the opening boundaries by global and flexible rules. The key is to identify the global flexible rules from the detected openings, composed by various combinations of alignments. As our method is oblivious of the type of architectural layout, it can be applied to both interior wall surfaces and exterior building facades. We demonstrate the flexibility of our approach in both outdoor and indoor scenes with a variety of opening layouts. The qualitative and quantitative evaluation results indicate the potential of the approach to be a general method in opening detection and modelling. However, this data-driven method suffers from the existence of occlusions and non-planar wall surfaces.

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“…The method provides relatively high levels of accuracy for the boundary points and single façades but does not create automatically complete building models. Similarly, Li et al detected the corners of openings based on the gradient of the number of points within the sliding window search along both horizontal and vertical directions [42]. Although the method can work properly for a case of partial missing data, it is limited for façades with rectilinear and repetitive openings.…”

Section: Building Energy Modellingmentioning

confidence: 99%

LiDAR point-cloud mapping of building façades for building energy performance simulation

O’Donnell

Truong-Hong

Boyle

et al. 2019

Automation in Construction

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Current processes that create Building Energy Performance Simulation (BEPS) models are time consuming and costly, primarily due to the extensive manual inputs required for model population. In particular, generation of geometric inputs for existing building models requires significant manual intervention due to the absence, or outdated nature of available data or digital measurements. Additionally, solutions based on Building Information Modelling (BIM) also require high quality and precise geometrically-based models, which are not typically available for existing buildings. As such, this work introduces a semi-automated BEPS input solution for existing building exteriors that can be integrated with other related technologies (such as BIM or CityGML) and deployed across an entire building stock. Within the overarching approach, a novel sub-process automatically transforms a point cloud obtained from a terrestrial laser scanner into a representation of a building's exterior façade geometry as input data for a BEPS engine.

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A Sliding Window Method for Detecting Corners of Openings From Terrestrial Lidar Data

Cited by 8 publications

References 27 publications

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

A Flexible Inference Machine for Global Alignment of Wall Openings

LiDAR point-cloud mapping of building façades for building energy performance simulation

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