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Cited by 580

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

Fleishman, Shachar, Cohen-Or, Daniel, Silva, Cláudio T.·2005·ACM Transactions on Graphics

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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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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CoChat. (2026). Citation check: Robust moving least-squares fitting with sharp features. https://app.cochat.ai/papers/10.1145/1073204.1073227
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Fleishman, S., Cohen-Or, D., & Silva, C. T. (2005). Robust moving least-squares fitting with sharp features. ACM Transactions on Graphics. https://doi.org/10.1145/1073204.1073227

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