Consensus Skeleton for Non-rigid Space-time Registration

We introduce the notion of consensus skeletons for non‐rigid space‐time registration of a deforming shape. Instead of basing the registration on point features, which are local and sensitive to noise, we adopt the curve skeleton of the shape as a global and descriptive feature for the task. Our meth...

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Published in:	Computer graphics forum Vol. 29; no. 2; pp. 635 - 644
Main Authors:	Zheng, Q., Sharf, A., Tagliasacchi, A., Chen, B., Zhang, H., Sheffer, A., Cohen-Or, D.
Format:	Journal Article
Language:	English
Published:	Oxford, UK Blackwell Publishing Ltd 01.05.2010
Subjects:	C (programming language) Deformation Frames I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-object representation I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—object representation, regisration and deformation Image processing systems Incompatibility Inductively coupled plasma regisration and deformation Registers Registration Sampling Spacetime Systems development Tasks
ISSN:	0167-7055, 1467-8659
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Abstract	We introduce the notion of consensus skeletons for non‐rigid space‐time registration of a deforming shape. Instead of basing the registration on point features, which are local and sensitive to noise, we adopt the curve skeleton of the shape as a global and descriptive feature for the task. Our method uses no template and only assumes that the skeletal structure of the captured shape remains largely consistent over time. Such an assumption is generally weaker than those relying on large overlap of point features between successive frames, allowing for more sparse acquisition across time. Building our registration framework on top of the low‐dimensional skeleton‐time structure avoids heavy processing of dense point or volumetric data, while skeleton consensusization provides robust handling of incompatibilities between per‐frame skeletons. To register point clouds from all frames, we deform them by their skeletons, mirroring the skeleton registration process, to jump‐start a non‐rigid ICP. We present results for non‐rigid space‐time registration under sparse and noisy spatio‐temporal sampling, including cases where data was captured from only a single view.
AbstractList	We introduce the notion of consensus skeletons for non-rigid space-time registration of a deforming shape. Instead of basing the registration on point features, which are local and sensitive to noise, we adopt the curve skeleton of the shape as a global and descriptive feature for the task. Our method uses no template and only assumes that the skeletal structure of the captured shape remains largely consistent over time. Such an assumption is generally weaker than those relying on large overlap of point features between successive frames, allowing for more sparse acquisition across time. Building our registration framework on top of the low-dimensional skeleton-time structure avoids heavy processing of dense point or volumetric data, while skeleton consensusization provides robust handling of incompatibilities between per-frame skeletons. To register point clouds from all frames, we deform them by their skeletons, mirroring the skeleton registration process, to jump-start a non-rigid ICP. We present results for non-rigid space-time registration under sparse and noisy spatio-temporal sampling, including cases where data was captured from only a single view. [PUBLICATION ABSTRACT] We introduce the notion of consensus skeletons for non-rigid space-time registration of a deforming shape. Instead of basing the registration on point features, which are local and sensitive to noise, we adopt the curve skeleton of the shape as a global and descriptive feature for the task. Our method uses no template and only assumes that the skeletal structure of the captured shape remains largely consistent over time. Such an assumption is generally weaker than those relying on large overlap of point features between successive frames, allowing for more sparse acquisition across time. Building our registration framework on top of the low-dimensional skeleton-time structure avoids heavy processing of dense point or volumetric data, while skeleton consensusization provides robust handling of incompatibilities between per-frame skeletons. To register point clouds from all frames, we deform them by their skeletons, mirroring the skeleton registration process, to jump-start a non-rigid ICP. We present results for non-rigid space-time registration under sparse and noisy spatio-temporal sampling, including cases where data was captured from only a single view. We introduce the notion of consensus skeletons for non‐rigid space‐time registration of a deforming shape. Instead of basing the registration on point features, which are local and sensitive to noise, we adopt the curve skeleton of the shape as a global and descriptive feature for the task. Our method uses no template and only assumes that the skeletal structure of the captured shape remains largely consistent over time. Such an assumption is generally weaker than those relying on large overlap of point features between successive frames, allowing for more sparse acquisition across time. Building our registration framework on top of the low‐dimensional skeleton‐time structure avoids heavy processing of dense point or volumetric data, while skeleton consensusization provides robust handling of incompatibilities between per‐frame skeletons. To register point clouds from all frames, we deform them by their skeletons, mirroring the skeleton registration process, to jump‐start a non‐rigid ICP. We present results for non‐rigid space‐time registration under sparse and noisy spatio‐temporal sampling, including cases where data was captured from only a single view.
Author	Sharf, A. Zheng, Q. Cohen-Or, D. Chen, B. Zhang, H. Sheffer, A. Tagliasacchi, A.
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Cites_doi	10.1145/142920.134003 10.1111/j.1467-8659.2008.01137.x 10.1145/1618452.1618521 10.1145/1399504.1360696 10.1111/j.1467-8659.2009.01384.x 10.1145/1073204.1073207 10.1145/1360612.1360698 10.1145/1276377.1276467 10.1145/1360612.1360697 10.1145/1073204.1073206 10.1145/1077534.1077546 10.1145/1516522.1516526 10.1145/1057432.1057456 10.1111/j.1467-8659.2008.01136.x 10.1109/5.18626 10.1109/ICCV.2005.20 10.1145/1409060.1409063 10.1109/CVPR.2008.4587696
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Copyright	2010 The Author(s) Journal compilation © 2010 The Eurographics Association and Blackwell Publishing Ltd. 2010 The Eurographics Association and Blackwell Publishing Ltd.
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References_xml	– reference: Beier T., Neely S.: Feature-based image metamorphosis. SIGGRAPH Comput. Graph. 26, 2 (1992), 35-42. – reference: Rabiner L. R.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77, 2 (1989), 257-286. – reference: Tagliasacchi A., Zhang H., Cohen-Or D.: Curve skeleton extraction from incomplete point cloud. ACM Transactions on Graphics, (Proceedings SIGGRAPH 2009) 28, 3 (2009), Article 71, 9 pages. – reference: Chang W., Zwicker M.: Automatic registration for articulated shapes. Eurographics Symp. on Geom. Processing 27, 5 (2008), 1459-1468. – reference: Bradley D., Popa T., Sheffer A., Heidrich W., Boubekeur T.: Markerless garment capture. ACM Trans. on Graphics 27, 3 (2008), 99-108. – reference: Pekelny Y., Gotsman C.: Articulated object reconstruction and markerless motion capture from depth video. Computer Graphics Forum 27, 2 (2008), 399-408. – reference: Li H., Adams B., Guibas L. J., Pauly M.: Robust single-view geometry and motion reconstruction. ACM Trans. on Graphics 28, 5 (2009). – reference: Wand M., Adams B., Ovsjanikov M., Berner A., Bokeloh M., Jenke P., Guibas L., Seidel H.-P., Schilling A.: Efficient reconstruction of non-rigid shape and motion from real-time 3D scanner data. ACM Trans. on Graphics 28, 2 (2009). – reference: De Aguiar E., Theobalt C., Thrun S., Seidel H.-P.: Automatic conversion of mesh animations into skeleton-based animations. Computer Graphics Forum (Special Issue of Eurographics) 27, 2 (2008), 389-397. – reference: Li H., Sumner R. W., Pauly M.: Global correspondence optimization for non-rigid registration of depth scans. Eurographics Symp. on Geom. Processing 27, 5 (2008), 1421-1430. – reference: Baran I., Popović J.: Automatic rigging and animation of 3d characters. ACM Trans. on Graphics 26, 3 (2007), 72. – reference: James D. L., Twigg C. D.: Skinning mesh animations. ACM Trans. on Graphics 24, 3 (2005), 399-407. – reference: Huang Q., Adams B., Wicke M., Guibas L. J.: Non-rigid registration under isometric deformations. Eurographics Symp. on Geom. Processing 27, 5 (2008), 1449-1457. – reference: Chang W., Zwicker M.: Range scan registration using reduced deformable models. Computer Graphics Forum (Special Issue of Eurographics) 28, 2 (2009), 447-456. – reference: Sharf A., Alcantara D. A., Lewiner T., Greif C., Sheffer A., Amenta N., Cohen-Or D.: Space-time surface reconstruction using incompressible flow. ACM Trans. on Graphics 27, 5 (2008), 1-10. – reference: Anguelov D., Srinivasan P., Koller D., Thrun S., Rodgers J., Davis J.: SCAPE: shape completion and animation of people. ACM Trans. on Graphics 24, 3 (2005), 408-416. – reference: De Aguiar E., Stoll C., Theobalt C., Ahmed N., Seidel H.-P., Thrun S.: Performance capture from sparse multi-view video. ACM Trans. on Graphics 27, 3 (2008). – reference: Süssmuth J., Winter M., Greiner G.: Reconstructing animated meshes from time-varying point clouds. Eurographics Symp. on Geom. 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Processing – ident: e_1_2_10_4_2 – ident: e_1_2_10_9_2 doi: 10.1111/j.1467-8659.2009.01384.x – ident: e_1_2_10_2_2 doi: 10.1145/1073204.1073207 – ident: e_1_2_10_17_2 – ident: e_1_2_10_7_2 doi: 10.1145/1360612.1360698 – ident: e_1_2_10_6_2 doi: 10.1145/1276377.1276467 – ident: e_1_2_10_10_2 doi: 10.1145/1360612.1360697 – volume: 28 start-page: 9 issue: 3 year: 2009 ident: e_1_2_10_26_2 article-title: Curve skeleton extraction from incomplete point cloud publication-title: ACM Transactions on Graphics, (Proceedings SIGGRAPH 2009) – volume: 27 start-page: 1469 issue: 5 year: 2008 ident: e_1_2_10_23_2 article-title: Reconstructing animated meshes from time‐varying point clouds publication-title: Eurographics Symp. on Geom. 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Snippet	We introduce the notion of consensus skeletons for non‐rigid space‐time registration of a deforming shape. Instead of basing the registration on point... We introduce the notion of consensus skeletons for non‐rigid space‐time registration of a deforming shape. Instead of basing the registration on point... We introduce the notion of consensus skeletons for non-rigid space-time registration of a deforming shape. Instead of basing the registration on point...
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SubjectTerms	C (programming language) Deformation Frames I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-object representation I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—object representation, regisration and deformation Image processing systems Incompatibility Inductively coupled plasma regisration and deformation Registers Registration Sampling Spacetime Systems development Tasks
Title	Consensus Skeleton for Non-rigid Space-time Registration
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