The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Point Cloud Skeletons via Laplacian Based Contraction
Abstract
We present an algorithm for curve skeleton extraction via Laplacian-based contraction. Our algorithm can be applied to surfaces with boundaries, polygon soups, and point clouds. We develop a contraction operation that is designed to work on generalized discrete geometry data, particularly point clouds, via local Delaunay triangulation and topological thinning. Our approach is robust to noise and can handle moderate amounts of missing data, allowing skeleton-based manipulation of point clouds without explicit surface reconstruction. By avoiding explicit reconstruction, we are able to perform skeleton-driven topology repair of acquired point clouds in the presence of large amounts of missing data. In such cases, automatic surface reconstruction schemes tend to produce incorrect surface topology. We show that the curve skeletons we extract provide an intuitive and easy-to-manipulate structure for effective topology modification, leading to more faithful surface reconstruction.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Topology matching for fully automatic similarity estimation of 3D shapes
Hilaga, M., Shinagawa, Y., Kohmura, T., & Kunii, T. L. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383282 · Cited by 2,187
Poisson Surface Reconstruction
Kazhdan, M. M., Bolitho, M., & Hoppe, H. (2006) · doi:10.2312/sgp/sgp06/061-070 · Cited by 2,125
Discrete Differential-Geometry Operators for Triangulated 2-Manifolds
Meyer, M., Desbrun, M., Schröder, P., & Barr, A. H. (2003) · Mathematics and Visualization · doi:10.1007/978-3-662-05105-4_2 · Cited by 1,889
A signal processing approach to fair surface design
Taubin, G. (1995) · Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH '95 · doi:10.1145/218380.218473 · Cited by 1,855
Reconstruction and representation of 3D objects with radial basis functions
Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., & Evans, T. R. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383266 · Cited by 1,852
Shilane, P., Min, P., Kazhdan, M., & Funkhouser, T. (2004) · Proceedings Shape Modeling Applications, 2004. · doi:10.1109/smi.2004.1314504 · Cited by 1,419
Floater, M. S. (2003) · Computer Aided Geometric Design · doi:10.1016/s0167-8396(03)00002-5 · Cited by 1,079
Multi-level partition of unity implicits
Ohtake, Y., Belyaev, A., Alexa, M., Turk, G., & Seidel, H.-P. (2005) · ACM SIGGRAPH 2005 Courses on - SIGGRAPH '05 · doi:10.1145/1198555.1198649 · Cited by 722
Amenta, N., Choi, S., & Kolluri, R. K. (2001) · Proceedings of the sixth ACM symposium on Solid modeling and applications · doi:10.1145/376957.376986 · Cited by 721
Laplacian smoothing and Delaunay triangulations
Field, D. A. (1988) · Communications in Applied Numerical Methods · doi:10.1002/cnm.1630040603 · Cited by 637
Cited by
P2P-NET: bidirectional point displacement net for shape transform
Yin, K., Huang, H., Cohen-Or, D., & Zhang, H. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201288 · Cited by 102
Self-citationRigNet: Neural Rigging for Articulated Characters
Xu, Z., Zhou, Y., Kalogerakis, E., Landreth, C., & Singh, K. (2020) · arXiv · doi:10.48550/arxiv.2005.00559 · Cited by 13
Learning skeletal articulations with neural blend shapes
Li, P., Aberman, K., Hanocka, R., Liu, L., Sorkine-Hornung, O., & Chen, B. (2021) · ACM Transactions on Graphics · doi:10.1145/3476576.3476702 · Cited by 4
Metadata
- Authors
- Cao, J., Tagliasacchi, A., Olson, M., Zhang, H., & Su, Z.
- Venue
- 2010 Shape Modeling International Conference
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 187-197
- OpenAlex
- W2144008209
- Cited by
- 313
- References
- 45
- FWCI
- 30.36
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Point Cloud Skeletons via Laplacian Based Contraction. https://app.cochat.ai/papers/10.1109/smi.2010.25
- The paper
- Cao, J., Tagliasacchi, A., Olson, M., Zhang, H., & Su, Z. (2010). Point Cloud Skeletons via Laplacian Based Contraction. 2010 Shape Modeling International Conference. https://doi.org/10.1109/smi.2010.25
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
