Template Assembly for Detailed Urban Reconstruction
We propose a new framework to reconstruct building details by automatically assembling 3D templates on coarse textured building models. In a preprocessing step, we generate an initial coarse model to approximate a point cloud computed using Structure from Motion and Multi View Stereo, and we model a...
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| Veröffentlicht in: | Computer graphics forum Jg. 34; H. 2; S. 217 - 228 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Oxford
Blackwell Publishing Ltd
01.05.2015
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | We propose a new framework to reconstruct building details by automatically assembling 3D templates on coarse textured building models. In a preprocessing step, we generate an initial coarse model to approximate a point cloud computed using Structure from Motion and Multi View Stereo, and we model a set of 3D templates of facade details. Next, we optimize the initial coarse model to enforce consistency between geometry and appearance (texture images). Then, building details are reconstructed by assembling templates on the textured faces of the coarse model. The 3D templates are automatically chosen and located by our optimization‐based template assembly algorithm that balances image matching and structural regularity. In the results, we demonstrate how our framework can enrich the details of coarse models using various data sets. |
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| AbstractList | We propose a new framework to reconstruct building details by automatically assembling 3D templates on coarse textured building models. In a preprocessing step, we generate an initial coarse model to approximate a point cloud computed using Structure from Motion and Multi View Stereo, and we model a set of 3D templates of facade details. Next, we optimize the initial coarse model to enforce consistency between geometry and appearance (texture images). Then, building details are reconstructed by assembling templates on the textured faces of the coarse model. The 3D templates are automatically chosen and located by our optimization-based template assembly algorithm that balances image matching and structural regularity. In the results, we demonstrate how our framework can enrich the details of coarse models using various data sets. |
| Author | Nan, Liangliang Wonka, Peter Jiang, Caigui Ghanem, Bernard |
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Information Theory, IEEE Transactions on 52, 12 (2006), 5406-5425. 7 – reference: Dalal N., Triggs B.: Histograms of oriented gradients for human detection. In CVPR (2005), vol. 1, pp. 886-893. 5 – reference: Dick A.R., Torr P. H. S., Cipolla R.: Modelling and interpretation of architecture from several images. Int. J. Comput. Vision 60, 2 (2004), 111-134. 2 – reference: Hastie T., Tibshirani R., Friedman J., Hastie T., Friedman J., Tibshirani R.: The elements of statistical learning, vol. 2. Springer, 2009. 7 – reference: Shen C.-H., Huang S.-S., FU H., HU S.-M.: Adaptive partitioning of urban facades. In ACM Transactions on Graphics (2011), vol. 30, p. 184. 2 – reference: Von Gioi R.G., Jakubowicz J., Morel J.-M., Randall G.: Lsd: A fast line segment detector with a false detection control. PAMI 32, 4 (2010), 722-732. 4 – reference: Pollefeys M. Nister D., Frahm E.: Detailed realtime urban 3D reconstruction from video. Int. J. Comput. Vision 78, 2-3 (2008), 143-167. 2 – reference: Wright J., Yang A.Y., Ganesh A., Sastry S.S., MA Y.: Robust face recognition via sparse representation. PAMI 31, 2 (2009), 210-227. 7 – reference: Lin H., Gao J., Zhou Y., LU G., YE M., Zhang C., Liu L., Yang R.: Semantic decomposition and reconstruction of residential scenes from lidar data. SIGGRAPH 32, 4 (2013). 1, 2 – reference: Xiao J., Fang T., Zhao P., Lhuillier M., Quan L.: Image-based street-side city modeling. ACM Transactions on Graphics 28, 5 (2009), 114:1-114:12. 1, 2, 3 – reference: Teboul O., Kokkinos I., Simon L., Koutsourakis P., Paragios N.: Parsing facades with shape grammars and reinforcement learning. PAMI 35, 7 (2013), 1744-1756. 2 – reference: Vanegas C.A., Aliaga D.G., Benes B.: Automatic extraction of manhattan-world building masses from 3d laser range scans. 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| Snippet | We propose a new framework to reconstruct building details by automatically assembling 3D templates on coarse textured building models. In a preprocessing... |
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| SubjectTerms | 3-D graphics Analysis and systems Assembling Assembly Buildings Categories and Subject Descriptors (according to ACM CCS) Construction 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 Image processing systems languages Optimization Surface layer Texture Three dimensional models Urban areas |
| Title | Template Assembly for Detailed Urban Reconstruction |
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