Consensus Skeleton for Non‐rigid Space‐time Registration
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.
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[5] can handle large gaps in 4D sequences and reduce high dimensionality of deformations through automatic construction of consensus skeletons.
“[5] that can handle large gaps in 4D sequences, reduce high dimensionality of deformations through automatic construction of consensus skeletons”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [5]
Recent space-time registrations focus on reducing the dimension of the deformation by constructing a low-dimensional representation, such as skeletons, bones and joints.
“Recent space-time registrations focus on reducing the dimension of the deformation (alleviating the over-fitting problem) by constructing a lowdimensional representation (e.g.skeletons [5], bones and joints [26]).”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [5]
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Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
Tam, G. K. L., Cheng, Z.-Q., Lai, Y.-K., Langbein, F. C., Liu, Y., Marshall, D., Martin, R. R., Sun, X.-F., & Rosin, P. L. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.310 · Cited by 679 · see citing works
- Authors
- Zheng, Q., Sharf, A., Tagliasacchi, A., Chen, B., Zhang, H., Sheffer, A., & Cohen‐Or, D.
- Affiliations
- Chinese Academy of Sciences (CN)
- Simon Fraser University (CA)
- University of British Columbia (CA)
- Tel Aviv University (IL)
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 2010-05
- Volume
- 29
- Issue
- 2
- Pages
- 635-644
- ISSN
- 0167-7055 (ISSN-L)
- 1467-8659
- OpenAlex
- W2146700972
- Semantic Scholar
- f5ae0d7aaf9c973716ef10b2fd838fc115a759ab
- Cited by
- 67
- References
- 33
- Citations by year
- 2025: 1 · 2022: 2 · 2021: 4 · 2020: 3 · 2019: 2 · 2018: 2 · 2017: 1 · 2016: 8 · 2015: 9 · 2014: 7 · 2013: 8 · 2012: 10
- FWCI
- 8.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Graphics Forum (publishedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- curve skeleton, point cloud registration, iterative closest point, noisy data
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