The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Consolidation of unorganized point clouds for surface reconstruction
Abstract
We consolidate an unorganized point cloud with noise, outliers, non-uniformities, and in particular interference between close-by surface sheets as a preprocess to surface generation, focusing on reliable normal estimation. Our algorithm includes two new developments. First, a weighted locally optimal projection operator produces a set of denoised, outlier-free and evenly distributed particles over the original dense point cloud, so as to improve the reliability of local PCA for initial estimate of normals. Next, an iterative framework for robust normal estimation is introduced, where a priority-driven normal propagation scheme based on a new priority measure and an orientation-aware PCA work complementarily and iteratively to consolidate particle normals. The priority setting is reinforced with front stopping at thin surface features and normal flipping to enable robust handling of the close-by surface sheet problem. We demonstrate how a point cloud that is well-consolidated by our method steers conventional surface generation schemes towards a proper interpretation of the input data.
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Cites
Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations
Ascher, U. M., & Petzold, L. R. (1998) · doi:10.1137/1.9781611971392 · Cited by 2,403
Self-citationReconstruction 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
Cited by
PU-Net: Point Cloud Upsampling Network
Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., & Heng, P.-A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00295 · Cited by 697
Self-citationPCPN et Learning Local Shape Properties from Raw Point Clouds
Guerrero, P., Kleiman, Y., Ovsjanikov, M., & Mitra, N. J. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13343 · Cited by 369
Patch-Based Progressive 3D Point Set Upsampling
Yifan, W., Wu, S., Huang, H., Cohen-Or, D., & Sorkine-Hornung, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00611 · Cited by 317
Self-citationPoint Cloud Skeletons via Laplacian Based Contraction
Cao, J., Tagliasacchi, A., Olson, M., Zhang, H., & Su, Z. (2010) · 2010 Shape Modeling International Conference · doi:10.1109/smi.2010.25 · Cited by 313
Self-citationEC-Net: An Edge-Aware Point Set Consolidation Network
Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., & Heng, P.-A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_24 · Cited by 300
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Metadata
- Authors
- Huang, H., Li, D., Zhang, H., Ascher, U., & Cohen-Or, D.
- Venue
- ACM Transactions on Graphics
- Published
- 2009-12
- Volume
- 28
- Issue
- 5
- Pages
- 1-7
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W1988317275
- Cited by
- 487
- References
- 2
- FWCI
- 15.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Huang, H., Li, D., Zhang, H., Ascher, U., & Cohen-Or, D. (2009). Consolidation of unorganized point clouds for surface reconstruction. ACM Transactions on Graphics. https://doi.org/10.1145/1618452.1618522
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