Lod3 Building Reconstruction From Multi-Source Images

Huang, Hai; Michelini, Mario; Schmitz, Matthias; Roth, Lukas; Mayer, Helmut

doi:10.5194/isprs-archives-xliii-b2-2020-427-2020

Cited by 16 publications

(11 citation statements)

References 32 publications

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“…While this does not impact the validity of the information extracted for each building, a mixture of drive‐by and fly‐by imaging would be required to ensure full building envelope coverage to avoid systemic underestimation of components and stock. Drone‐based fly‐by building detection approaches are now fairly advanced in reconstructing building geometry from visual images (H. Huang et al., 2020). Remaining challenges in complementing drive‐by methods with fly‐by imaging are those relating to swarm dynamics.…”

Section: Building Stock Characterization: Relevance Challenges and Op...mentioning

confidence: 99%

A scalable data collection, characterization, and accounting framework for urban material stocks

Arbabi

Lanau

Meyers

et al. 2021

J of Industrial Ecology

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Building stocks represent an extensive reservoir of secondary resources. However, common bottom‐up characterization of these, often based on archetypal classification of buildings and their corresponding material intensity, are still not suitable to adequately inform circular economic strategies. Indeed, these approaches typically result in a loss of building‐specific details, and a building stock characterization in terms of material mass, for example, glass, rather than component, for example, window. To deliver this higher resolution of details, a scalable approach to urban stock characterization, that enables a bottom‐up estimation of building stocks at the building component level, is needed. In this paper, we present a framework to automate the characterization of urban stock. By using and combining a mobile‐sensing approach with computer vision, urban stocks can be captured as 3D surface maps allowing the identification and semantic classification of stock objects, components, and materials. We demonstrate the potential of this framework through a case study of a neighborhood in Sheffield, UK, by using a prototype workflow comprising a custom‐made mobile‐sensing platform and an existing suite of neural network models to calculate an estimate count of buildings external doors and windows. The prototype implementation of the framework achieves comparable total and building‐level component counts with those achieved through manual human counts. Such automated estimation of components enables an understanding of opportunities across the circular economic hierarchies and informs stakeholders across the supply chain to better prepare for the implementation of circular strategies including building refurbishments.

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Section: Building Stock Characterization: Relevance Challenges and Op...mentioning

confidence: 99%

A scalable data collection, characterization, and accounting framework for urban material stocks

Arbabi

Lanau

Meyers

et al. 2021

J of Industrial Ecology

View full text Add to dashboard Cite

show abstract

“…In an approach that uses shell models, in which shells represent building envelopes, Huang et al 34 retrieve 3D contours from multi-source images for constructing Breps that represent the building’s 3D shape. This method underlines the advantages of using Breps in digital building-envelope reconstruction, mainly because Breps help define roof and facades within the same model.…”

Section: Related Workmentioning

confidence: 99%

“…Other methods, however, appear to tackle similar problems by entailing alternative approaches.Bassier et al,32 for instance, propose a "Conditional Random Field" that evaluates the context of each building's wall in order to determine its position among complex spatial configurations.Laing et al33 use Poisson algorithms to turn point-cloud data into uniform meshes, which are later sliced in order to obtain contour data usable in the Computer Numerical Control machining of mockups. Meshes serve then as a bridge for retrieving geometric building data.In an approach that uses shell models, in which shells represent building envelopes, Huang et al34 retrieve 3D contours from multi-source images for constructing Breps that represent the building's 3D shape. This method underlines the advantages of using Breps in digital building-envelope reconstruction, mainly because Breps help define roof and facades within the same model.…”

mentioning

confidence: 99%

Parametric point-cloud slicing for facade retrofitting

Bohórquez

Derigent

El-Haouzi

2021

International Journal of Architectural Computing

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This work presents a method for retrieving 3D building contours usable in facade retrofitting projects, which uses a parametric modeling workflow that utilizes a point-cloud slicing method to retrieve such 3D contours. Since current commitments by European governments seek to reduce energy consumption as a means to reduce carbon emissions from building stock by 2050, facade retrofitting appears as an alternative for addressing operational and embedded building emissions. Within such a context, the main contribution of this work consists of a workflow and a 3D reconstruction solution that uses a parametric environment for capturing building topology and bypassing ground-level occlusions. A real case study and a strategy for converting 3D building contours into Industry Foundation Classes entities, directly from the parametric modeling environment, served as a scenario for testing the capabilities of a Grasshopper solution and open new perspectives for this approach.

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“…The principal challenges lie in the incompleteness of acquired MLS data, where often only the frontal facade is measured. This feature hinders from-scratch reconstruction, which assumes very high point cloud coverage [22,14,19]. Another challenge concerns single-object semantics, typically derived in the semantic segmentation process.…”

Section: Introductionmentioning

confidence: 99%

Tum-Façade: Reviewing and Enriching Point Cloud Benchmarks for Façade Segmentation

Wysocki

Hoegner

Stilla

2022

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

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Abstract. Point clouds are widely regarded as one of the best dataset types for urban mapping purposes. Hence, point cloud datasets are commonly investigated as benchmark types for various urban interpretation methods. Yet, few researchers have addressed the use of point cloud benchmarks for façade segmentation. Robust façade segmentation is becoming a key factor in various applications ranging from simulating autonomous driving functions to preserving cultural heritage. In this work, we present a method of enriching existing point cloud datasets with façade-related classes that have been designed to facilitate façade segmentation testing. We propose how to efficiently extend existing datasets and comprehensively assess their potential for façade segmentation. We use the method to create the TUM-FAÇADE dataset, which extends the capabilities of TUM-MLS-2016. Not only can TUM-FAÇADE facilitate the development of point-cloud-based façade segmentation tasks, but our procedure can also be applied to enrich further datasets.

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Lod3 Building Reconstruction From Multi-Source Images

Cited by 16 publications

References 32 publications

A scalable data collection, characterization, and accounting framework for urban material stocks

A scalable data collection, characterization, and accounting framework for urban material stocks

Parametric point-cloud slicing for facade retrofitting

Tum-Façade: Reviewing and Enriching Point Cloud Benchmarks for Façade Segmentation

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