The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Robust moving least-squares fitting with sharp features
Abstract
We introduce a robust moving least-squares technique for reconstructing a piecewise smooth surface from a potentially noisy point cloud. We use techniques from robust statistics to guide the creation of the neighborhoods used by the moving least squares (MLS) computation. This leads to a conceptually simple approach that provides a unified framework for not only dealing with noise, but also for enabling the modeling of surfaces with sharp features.Our technique is based on a new robust statistics method for outlier detection: the forward-search paradigm. Using this powerful technique, we locally classify regions of a point-set to multiple outlier-free smooth regions. This classification allows us to project points on a locally smooth region rather than a surface that is smooth everywhere, thus defining a piecewise smooth surface and increasing the numerical stability of the projection operator. Furthermore, by treating the points across the discontinuities as outliers, we are able to define sharp features. One of the nice features of our approach is that it automatically disregards outliers during the surface-fitting phase.
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Cites
Robust Regression and Outlier Detection
Piepel, G. F. (1989) · Technometrics · doi:10.1080/00401706.1989.10488524 · Cited by 6,144
Robust Regression and Outlier Detection
Rousseeuw, P. J., & Leroy, A. M. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/0471725382 · Cited by 5,609
A volumetric method for building complex models from range images
Curless, B., & Levoy, M. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237269 · Cited by 3,124
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
Implicit fairing of irregular meshes using diffusion and curvature flow
Desbrun, M., Meyer, M., Schröder, P., & Barr, A. H. (1999) · Proceedings of the 26th annual conference on Computer graphics and interactive techniques - SIGGRAPH '99 · doi:10.1145/311535.311576 · Cited by 1,538
The ball-pivoting algorithm for surface reconstruction
Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., & Taubin, G. (1999) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.817351 · Cited by 1,495
Self-citationZippered polygon meshes from range images
Turk, G., & Levoy, M. (1994) · Proceedings of the 21st annual conference on Computer graphics and interactive techniques - SIGGRAPH '94 · doi:10.1145/192161.192241 · Cited by 1,326
A new Voronoi-based surface reconstruction algorithm
Amenta, N., Bern, M., & Kamvysselis, M. (1998) · Proceedings of the 25th annual conference on Computer graphics and interactive techniques - SIGGRAPH '98 · doi:10.1145/280814.280947 · Cited by 901
Identifying Multiple Outliers in Multivariate Data
Hadi, A. S. (1992) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1992.tb01449.x · Cited by 761
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Towards 3D Point cloud based object maps for household environments
Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.08.005 · Cited by 1,084
Reassembling fractured objects by geometric matching
Huang, Q.-X., Flöry, S., Gelfand, N., Hofer, M., & Pottmann, H. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141925 · Cited by 329
Non‐Rigid Registration Under Isometric Deformations
Huang, Q., Adams, B., Wicke, M., & Guibas, L. J. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01285.x · Cited by 279
3D reconstruction methods for digital preservation of cultural heritage: A survey
Gomes, L., Regina Pereira Bellon, O., & Silva, L. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.03.023 · Cited by 267
Robust principal axes determination for point-based shapes using least median of squares
Liu, Y.-S., & Ramani, K. (2009) · Computer-Aided Design · doi:10.1016/j.cad.2008.10.012 · Cited by 46
Metadata
- Authors
- Fleishman, S., Cohen-Or, D., & Silva, C. T.
- Venue
- ACM Transactions on Graphics
- Published
- 2005-07
- Volume
- 24
- Issue
- 3
- Pages
- 544-552
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2058524213
- Cited by
- 580
- References
- 31
- FWCI
- 31.11
- Type
- journal article
- Language
- en
- Open access
- closed
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