Automatic Modeling of Urban Facades from Raw LiDAR Point Data
Modeling 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 regularit...
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| Veröffentlicht in: | Computer graphics forum Jg. 35; H. 7; S. 269 - 278 |
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01.10.2016
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| Abstract | Modeling 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. |
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| AbstractList | Modeling 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. Modeling 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. |
| Author | Ye, N. Wei, M. Yi, C. Xu, Y. Xie, X. Wang, J. Remil, O. |
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| Cites_doi | 10.1145/2421636.2421642 10.1109/TPAMI.2004.1262332 10.1145/882262.882324 10.1145/1778765.1778831 10.1109/34.121791 10.1109/ICCV.2009.5459145 10.1145/2070781.2024218 10.1137/0715063 10.1109/CVPRW.2012.6238908 10.1145/1360612.1360642 10.1007/s11263-011-0474-7 10.1109/TPAMI.2013.2296310 10.1016/j.patcog.2007.04.003 10.1109/CVPR.2007.382975 10.1145/2010324.1964947 10.1145/1618452.1618460 10.1145/1141911.1141931 10.1145/2766995 10.1111/j.1467-8659.2007.01016.x |
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| SubjectTerms | 3-D graphics Analysis and systems Automation Buildings Categories and Subject Descriptors (according to ACM CCS) Computer based modeling Facades I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems languages Lidar Measurement Modelling Optimization Raw Regularity Repetition Studies Three dimensional models Urban areas |
| Title | Automatic Modeling of Urban Facades from Raw LiDAR Point Data |
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