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dc.contributor.authorWang, Junen_US
dc.contributor.authorXu, Yabinen_US
dc.contributor.authorRemil, Oussamaen_US
dc.contributor.authorXie, Xingyuen_US
dc.contributor.authorYe, Nanen_US
dc.contributor.authorWei, Mingqiangen_US
dc.contributor.editorEitan Grinspun and Bernd Bickel and Yoshinori Dobashien_US
dc.date.accessioned2016-10-11T05:20:05Z
dc.date.available2016-10-11T05:20:05Z
dc.date.issued2016
dc.identifier.issn1467-8659
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.13024
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13024
dc.description.abstractModeling of urban facades from raw LiDAR point data remains active due to its challenging nature. In this paper, we propose an automatic yet robust 3D modeling approach for urban facades with raw LiDAR point clouds. The key observation is that building facades often exhibit repetitions and regularities. We hereby formulate repetition detection as an energy optimization problem with a global energy function balancing geometric errors, regularity and complexity of facade structures. As a result, repetitive structures are extracted robustly even in the presence of noise and missing data. By registering repetitive structures, missing regions are completed and thus the associated point data of structures are well consolidated. Subsequently, we detect the potential design intents (i.e., geometric constraints) within structures and perform constrained fitting to obtain the precise structure models. Furthermore, we apply structure alignment optimization to enforce position regularities and employ repetitions to infer missing structures. We demonstrate how the quality of raw LiDAR data can be improved by exploiting data redundancy, and discovering high level structural information (regularity and symmetry). We evaluate our modeling method on a variety of raw LiDAR scans to verify its robustness and effectiveness.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.5 [Computer Graphics]
dc.subjectComputational Geometry and Object Modeling
dc.subjectGeometric algorithms
dc.subjectlanguages
dc.subjectand systems
dc.titleAutomatic Modeling of Urban Facades from Raw LiDAR Point Dataen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersModeling
dc.description.volume35
dc.description.number7
dc.identifier.doi10.1111/cgf.13024
dc.identifier.pages269-278


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