Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
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SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
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SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
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SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
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SMI '03 Proceedings of the Shape Modeling International 2003
Fast Surface Reconstruction Using the Level Set Method
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
SMI '04 Proceedings of the Shape Modeling International 2004
Surface Reconstruction of Noisy and Defective Data Sets
VIS '04 Proceedings of the conference on Visualization '04
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Proceedings of the 2005 ACM symposium on Solid and physical modeling
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Examining the uncertainty of the recovered surface normal in three light photometric stereo
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ACM SIGGRAPH 2007 papers
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ACM SIGGRAPH 2007 papers
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
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ACM SIGGRAPH 2008 papers
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2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling
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IEEE Transactions on Image Processing
Technical Section: Robust normal estimation for point clouds with sharp features
Computers and Graphics
Technical Section: Orienting unorganized points for surface reconstruction
Computers and Graphics
Technical Section: Visibility of noisy point cloud data
Computers and Graphics
Point Cloud Skeletons via Laplacian Based Contraction
SMI '10 Proceedings of the 2010 Shape Modeling International Conference
Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
SMI 2013: Point cloud normal estimation via low-rank subspace clustering
Computers and Graphics
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We present a global method for consistently orienting a defective raw point set with noise, non-uniformities and thin sharp features. Our method seamlessly combines two simple but effective techniques-constrained Laplacian smoothing and visibility voting-to tackle this challenge. First, we apply a Laplacian contraction to the given point cloud, which shrinks the shape a little bit. Each shrunk point corresponds to an input point and shares a visibility confidence assigned by voting from multiple viewpoints. The confidence is increased (resp. decreased) if the input point (resp. its corresponding shrunk point) is visible. Then, the initial normals estimated by principal component analysis are flipped according to the contraction vectors from shrunk points to the corresponding input points and the visibility confidence. Finally, we apply a Laplacian smoothing twice to correct the orientation of points with zero or low confidence. Our method is conceptually simple and easy to implement, without resorting to any complicated data structures and advanced solvers. Numerous experiments demonstrate that our method can orient the defective raw point clouds in a consistent manner. By taking advantage of our orientation information, the classical implicit surface reconstruction algorithms can faithfully generate the surface.