Robust Segmentation of Multiple Intersecting Manifolds from Unoriented Noisy Point Clouds
We present a method for extracting complex manifolds with an arbitrary number of (self‐) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three‐step process. First, small flat neighbourhoods of all possible orientations are created around all po...
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| Veröffentlicht in: | Computer graphics forum Jg. 33; H. 1; S. 73 - 87 |
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| Abstract | We present a method for extracting complex manifolds with an arbitrary number of (self‐) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three‐step process. First, small flat neighbourhoods of all possible orientations are created around all points. Next, neighbourhoods are assembled into larger quasi‐flat patches, whose overlaps give the global connectivity structure of the point cloud. Finally, curved manifolds are extracted from the patch connectivity graph via a multiple‐source flood fill. The manifolds can be reconstructed into meshed surfaces using standard existing surface reconstruction methods. We demonstrate the speed and robustness of our method on several point clouds, with applications in point cloud segmentation, denoising and medial surface reconstruction.
We present a method for extracting complex manifolds with an arbitrary number of (self) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three step process. First, small flat neighborhoods of all possible orientations are created around all points. Next, neighborhoods are assembled into larger quasi‐flat patches, whose overlaps determine the global connectivity structure of the point cloud. Finally, curved manifolds, as well as their intersection curves, are extracted from the patch connectivity graph via a multiple‐source flood fill. The extracted manifolds can be straightforwardly reconstructed into polygonal surfaces using standard surface reconstruction methods. |
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| AbstractList | We present a method for extracting complex manifolds with an arbitrary number of (self‐) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three‐step process. First, small flat neighbourhoods of all possible orientations are created around all points. Next, neighbourhoods are assembled into larger quasi‐flat patches, whose overlaps give the global connectivity structure of the point cloud. Finally, curved manifolds are extracted from the patch connectivity graph via a multiple‐source flood fill. The manifolds can be reconstructed into meshed surfaces using standard existing surface reconstruction methods. We demonstrate the speed and robustness of our method on several point clouds, with applications in point cloud segmentation, denoising and medial surface reconstruction.
We present a method for extracting complex manifolds with an arbitrary number of (self) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three step process. First, small flat neighborhoods of all possible orientations are created around all points. Next, neighborhoods are assembled into larger quasi‐flat patches, whose overlaps determine the global connectivity structure of the point cloud. Finally, curved manifolds, as well as their intersection curves, are extracted from the patch connectivity graph via a multiple‐source flood fill. The extracted manifolds can be straightforwardly reconstructed into polygonal surfaces using standard surface reconstruction methods. We present a method for extracting complex manifolds with an arbitrary number of (self‐) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three‐step process. First, small flat neighbourhoods of all possible orientations are created around all points. Next, neighbourhoods are assembled into larger quasi‐flat patches, whose overlaps give the global connectivity structure of the point cloud. Finally, curved manifolds are extracted from the patch connectivity graph via a multiple‐source flood fill. The manifolds can be reconstructed into meshed surfaces using standard existing surface reconstruction methods. We demonstrate the speed and robustness of our method on several point clouds, with applications in point cloud segmentation, denoising and medial surface reconstruction. We present a method for extracting complex manifolds with an arbitrary number of (self-) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three-step process. First, small flat neighbourhoods of all possible orientations are created around all points. Next, neighbourhoods are assembled into larger quasi-flat patches, whose overlaps give the global connectivity structure of the point cloud. Finally, curved manifolds are extracted from the patch connectivity graph via a multiple-source flood fill. The manifolds can be reconstructed into meshed surfaces using standard existing surface reconstruction methods. We demonstrate the speed and robustness of our method on several point clouds, with applications in point cloud segmentation, denoising and medial surface reconstruction. [PUBLICATION ABSTRACT] |
| Author | Jalba, A. Telea, A. Kustra, J. |
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| Copyright | 2013 The Authors Computer Graphics Forum © 2013 The Eurographics Association and John Wiley & Sons Ltd. Copyright © 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| References_xml | – reference: [TdSL00] Tenenbaum J. B., de Silva V., Langford J. C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500 (2000), 2319-2323. – reference: [Sot07] Sotoodeh S.: Hierarchical clustered outlier detection in laser scanner point clouds. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36, (2007), 383-388. – reference: [MT00] Medioni G., Tang C.-K.: Tensor voting: Theory and applications. In Proceedings of RFIA (2000). – reference: [KBH06] Kazhdan M., Bolitho M., Hoppe H.: Poisson surface reconstruction. In Proceedings of SGP (2006), pp. 61-70. – reference: [RH07] Ringbeck T., Hagebeuker B.: A 3D time of flight camera for object detection. Measurement 9 (2007), 867-879. – reference: [SSZCO10] Shalom S., Shamir A., Zhang H., Cohen-Or D.: Cone carving for surface reconstruction. ACM TOG 29, 6 (2010), 547-555. – reference: [WHH10] Weber C., Hahmann S., Hagen H.: Sharp feature detection in point clouds. In Proceedings of SMA (2010), pp. 175-186. – reference: [VMS05] Vidal R., Ma Y., Sastry S.: Generalized principal component analysis. IEEE TPAMI 27, 12 (2005), 1945-1959. – reference: [RT08b] Reniers D., Telea A.: Robust segmentation of voxel shapes using medial surfaces. In Proceedings of the SMI (2008). – reference: [DLRW09] Dey T., Li K., Ramos E., Wenger R.: Isotopic reconstruction of surfaces with boundaries. CGF 28, 5 (2009), 1371-1382. – reference: [JR07] Jalba A., Roerdink J.: Efficient surface reconstruction using Coulomb potentials. IEEE TVCG 13, 6 (2007), 1512-1519. – reference: [BMR*99] Bernardini F., Mittleman J., Rushmeier H., Silva C., Taubin G.: The ball-pivoting algorithm for surface reconstruction. IEEE TVCG 5, 4 (1999), 349-359. – reference: [GP05] Gauss C. F., Pesic P.: General Investigations of Curved Surfaces. Dover Books on Mathematics. Dover Publ., Mineola, NY, USA, 2005. – reference: [SP99] Siddiqi K., Pizer S.: Medial Representations: Mathematics, Algorithms and Applications. Springer, Berlin, Heidelberg, Germany, 1999. – reference: [CLK09] Chang M.-C., Leymarie F. F., Kimia B. B.: Surface reconstruction from point clouds by transforming the medial scaffold. CVIU 113, 11 (2009), 1130-1146. – reference: [PNF*08] Pollefeys M., Nistér D., Frahm J. M., Akbarzadeh A., Mordohai P., Clipp B., Engels C., Gallup D., Kim S. J., Merrell P., Salmi C., Sinha S., Talton B., Wang L., Yang Q., Stewénius H., Yang R., Welch G., Towles H.: Detailed real-time urban 3D reconstruction from video. International Journal of Computer Vision 78, 2-3 (2008), 143-167. – reference: [Haw80] Hawkins D. M.: Identification of Outliers. Chapman and Hall, London, 1980. – reference: [KGB06] Kushnir D., Galun M., Brandt A.: Fast multiscale clustering and manifold identification. Pattern Recognition 39, 10 (2006), 1876-1891. – reference: [UH07] Unnikrishnan R., Hebert M.: Denoising manifold and non-manifold point clouds. In Proceedings of BMVC (2007). – reference: [CSD04] Cohen-Steiner D., Da F.: A greedy Delaunay-based surface reconstruction algorithm. Visual Computer 20, 1 (2004), 4-16. – reference: [WK04] Wu J., Kobbelt L.: Optimized sub-sampling of point sets for surface splatting. CGF 23, 3 (2004), 643-652. – reference: [PSS*03] Pizer S., Siddiqi K., Szekely G., Damon J., Zucker S.: Multiscale medial loci and their properties. IJCV 55, 2-3 (2003), 155-179. – reference: [SHG06] Sitek A., Huesman R. H., Gullberg G. T.: Tomographic reconstruction using an adaptive tetrahedral mesh defined by a point cloud. IEEE Transactions on Medical Imaging 25, 9 (2006), 1172-1179. – reference: [UH03] Unnikrishnan R., Hebert M.: Robust extraction of multiple structures from non-uniformly sampled data. 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| Title | Robust Segmentation of Multiple Intersecting Manifolds from Unoriented Noisy Point Clouds |
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