2021
DOI: 10.5194/isprs-annals-v-4-2021-17-2021
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Plastic Surgery for 3d City Models: A Pipeline for Automatic Geometry Refinement and Semantic Enrichment

Abstract: Abstract. Nowadays, the number of connected devices providing unstructured data is rapidly rising. These devices acquire data with a temporal and spatial resolution at an unprecedented level creating an influx of geoinformation which, however, lacks semantic information. Simultaneously, structured datasets like semantic 3D city models are widely available and assure rich semantics and high global accuracy but are represented by rather coarse geometries. While the mentioned downsides curb the usability of these… Show more

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Cited by 4 publications
(3 citation statements)
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“…• Lack of georeferencing: Many benchmarks do not contain information about the position with reference to the global CRS; They are often provided in a local CRS, as shown in Table 2. This excludes or at best hinders a comparison of methods using multimodal sources, such as point clouds in conjunction with 2D or 3D GIS datasets, such as in (Murtiyoso and Grussenmeyer, 2019) or (Wysocki et al, 2021a).…”
Section: Potential Of Existing Point Cloud Benchmarks For Fac ¸Ade Se...mentioning
confidence: 99%
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“…• Lack of georeferencing: Many benchmarks do not contain information about the position with reference to the global CRS; They are often provided in a local CRS, as shown in Table 2. This excludes or at best hinders a comparison of methods using multimodal sources, such as point clouds in conjunction with 2D or 3D GIS datasets, such as in (Murtiyoso and Grussenmeyer, 2019) or (Wysocki et al, 2021a).…”
Section: Potential Of Existing Point Cloud Benchmarks For Fac ¸Ade Se...mentioning
confidence: 99%
“…On the other hand, 3D point clouds are deemed among the best data sources for urban mapping purposes, as they yield an immediate 3D representation (Xu and Stilla, 2021). Of the particular interest are point clouds acquired by MLS vehicles thanks to their, high temporal resolution, and the density of the street-level point clouds (Wysocki et al, 2021a). This has led to a recent growth in interest in developing methods of parsing fac ¸ades using point clouds (Martinovic et al, 2015, Fan et al, 2021, Zolanvari and Laefer, 2016, especially using machine learning methods (Matrone et al, 2020, Liu et al, 2020.…”
Section: Related Workmentioning
confidence: 99%
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