Unlocking Point Cloud Potential: Fusing MLS Point Clouds With Semantic 3d Building Models While Considering Uncertainty

Wysocki, Olaf; Xu, Yangsheng; Stilla, Uwe

doi:10.5194/isprs-annals-viii-4-w2-2021-45-2021

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Papers advising the insertion of thermal data into building information models (BIMs) and digital twins of buildings have appeared more frequently in the last 5-10 years [1][2][3][4][5]. Nevertheless, thermal point clouds (TPC) have recently been included in papers and conferences as a new challenging topic with tremendous potential in future applications in architecture, engineering and construction (AEC).…”

Section: Apparent Temperature In 3d Thermal Point Cloudsmentioning

confidence: 99%

Correction of Temperature from Infrared Cameras for More Precise As-Is 3D Thermal Models of Buildings

et al. 2023

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This paper studies how to create precise 3D thermal maps of building interiors by correcting the raw apparent temperature values yielded by the thermal cameras. This matter has not, to date, been dealt with in the literature, which leads us to conclude that the current methodologies that obtain thermal point clouds are incomplete and imprecise. Without a suitable correction, any further energy parameter calculation obtained from the thermal point cloud is incorrect. This paper presents a method that deals with important aspects to be corrected, such as the repeatability of thermal cameras, the use of the true emissivity of the materials sensed, and the inclusion of the reflected radiant energy caused by the environment. The method has been successfully tested in several indoor scenes using a thermal scanning platform. The results show that significant corrections of up to 8% of the raw temperature values must be carried out in the final thermal model, thus justifying the need for the correction. As an application of the method, an empirical calculation and a comparison of transmittances with and without temperature corrections are presented at the end of the paper. In this case, the relative errors with respect to the average nominal U-value decrease from 94% to 11%. The general conclusion is that precise calculations of energy parameters in which infrared cameras are involved must take these corrections to temperature into account.

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Section: Apparent Temperature In 3d Thermal Point Cloudsmentioning

confidence: 99%

Correction of Temperature from Infrared Cameras for More Precise As-Is 3D Thermal Models of Buildings

et al. 2023

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“…A good example of such a data interdependence is explained succinctly in (Tuttas et al, 2015), where an IFC model is compared against a point cloud model for monitoring construction progress. Another example of the need for data interoperability is shown in (Wysocki et al, 2021) where data from MLS point cloud is combined with semantic city models to improve the quality of 3D data capture.…”

Section: 2mentioning

confidence: 99%

Semantically Enriched Voxels as a Common Representation for Comparison and Evaluation of 3d Building Models

Heeramaglore

Kolbe²

2022

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

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Abstract. 3D city models are fast becoming a key factor for planning, design and analysis in the AEC industry, and urban informatics. Data are being acquired from a variety of sources and each consequent representation contains inherent information about its location, geometry, structure and usability. These digital representations can range from unstructured models with very little semantic data such as 3D point clouds, and 3D mesh models or they can be highly structured and semantically enriched models following CityGML and IFC/BIM standards. Matching these diverse representations against one another can facilitate information flow and eventually lead to a coherent interconnected Digital twin. Consistency measures would be needed to compare one representation against another. For comparison of any kind, a common baseline needs to be established. A tentative approach, as outlined in this paper, is to convert all the model types into a common representation such as ‘volumetric pixels’ or ‘voxels’ but the concept of ‘voxel’ as we know it, is not enough to deal with the rich semantic and organizational structure of modelling representations such as IFC and CityGML. Voxelisation is a complex process and conventional conversion methods concentrate on translating the geometry between representation types but the semantic and class hierarchy information is usually not translated. This paper assesses the need for matching 3D models using a common representation (voxels), discusses the challenges of the voxelisation process and proposes the concept of a ‘RichVoxel’.

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“…This, in turn, allows us to create buffers around building footprints extracted from vector GIS datasets (e.g., CityGML building models or OpenStreetMap (OSM) buildings). This ensures to reject a significant proportion of the point clouds and cluster the building-related points per building object, while addressing global point positioning inaccuracies (Wysocki et al, 2021b). Alternatively, when point clouds are not georeferenced or GIS datasets are unavailable, existing benchmark points, annotated as buildings, can be used as a pre-cluster for fac ¸ade-related points.…”

Section: Creating An Extended Benchmark Datasetmentioning

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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Unlocking Point Cloud Potential: Fusing MLS Point Clouds With Semantic 3d Building Models While Considering Uncertainty

Cited by 6 publications

References 24 publications

Correction of Temperature from Infrared Cameras for More Precise As-Is 3D Thermal Models of Buildings

Correction of Temperature from Infrared Cameras for More Precise As-Is 3D Thermal Models of Buildings

Semantically Enriched Voxels as a Common Representation for Comparison and Evaluation of 3d Building Models

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

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