Robust moving least-squares fitting with sharp features

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

687 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

View Paper: https://www.semanticscholar.org/paper/254276e646bad8bd9f035741e63ea5991cedbaff

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

A Survey of Surface Reconstruction from Point Clouds (2017, methodology): “The Robust MLS (RMLS) method [FCOS05] reproduces features by observing that points lying on two different smooth components can also be seen as outliers with respect to each other.”
Scale Space Meshing of Raw Data Point Sets (2011, methodology): “Variations of the MLS algo-rithm for denoising point sampled surfaces and preserving edges were proposed in [FCOS05], [GTE ∗ 06], [OGG09], [LCOL07], [GG07].”
Fast and Effective Feature-Preserving Mesh Denoising (2007, methodology): “Surface smoothing or denoising methods corresponding to the point-based models were also investigated [24], [25].”
Direct visibility of point sets (2007, methodology): “…compute visibility of a point cloud is to reconstruct the surface [Hoppe et al. 1992; Bernardini et al. 1999; Curless and Levoy 1996; Carr et al. 2001; Amenta et al. 2001; Amenta et al. 2002; Amenta and Kil 2004; Fleishman et al. 2005] and determine visibility on the reconstructed triangular mesh.”
Towards 3D Point cloud based object maps for household environments (2008, methodology): “Another Robust Moving Least Squares algorithm is described in [7], which differs from ours in that it uses LMedS instead of our sample consensus (SAC) based model fitting approach.”
Reassembling fractured objects by geometric matching (2006, methodology): “We do not rely on a user defined constant here but use a strategy similar to the method in [Fleishman et al. 2005] which takes into account the noise level of the surface.”
EC-Net: an Edge-aware Point set Consolidation Network (2018, methodology): “To consolidate a point set in an edge-aware manner, some methods detected/sensed the sharp edges and arranged points deliberatively along edges to preserve their sharpness [16,17,18,19].”
Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST (2010, background): “Computing surface normals can be challenging near boundaries, where the underlying surface is discontinuous or changes orientation, because data from other surfaces can bias the estimate [27].”
Query-driven iterated neighborhood graph search for large scale indexing (2012, methodology): “The 2D contours are further vectorized by ﬁtting line segments to the points with robust moving least-squares [12] and represented by a set of points with 2D coordinates.”
Algebraic point set surfaces (2007, methodology): “Finally, we developed a simple and powerful extension of [Fleishman et al. 2005] to robustly handle sharp features, such as boundaries and creases, with a built-in sharpness control (figure 1).”
Consolidation of unorganized point clouds for surface reconstruction (2009, methodology): “Thus, previous approaches to sharp feature detection, e.g., [Page et al. 2002; Fleishman et al. 2005], while generally robust, are not designed to handle such cases.”
Vision-Based Offline-Online Perception Paradigm for Autonomous Driving (2015, methodology): “Within this step we also re-estimate the position of all remaining points by applying a Moving Least Square method (MLS) [14].”