Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
The approximation power of moving least-squares
Mathematics of Computation
Meshless parameterization and surface reconstruction
Computer Aided Geometric Design
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Proceedings of the conference on Visualization '01
Efficient simplification of point-sampled surfaces
Proceedings of the conference on Visualization '02
Multi-level partition of unity implicits
ACM SIGGRAPH 2003 Papers
Shape modeling with point-sampled geometry
ACM SIGGRAPH 2003 Papers
Image deformation using moving least squares
ACM SIGGRAPH 2006 Papers
Error bounds and optimal neighborhoods for MLS approximation
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Data-dependent MLS for faithful surface approximation
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
NURBS curve shape modification and fairness evaluation
WSEAS Transactions on Computers
Robust denoising of point-sampled surfaces
WSEAS Transactions on Computers
Curve generation and modification based on radius of curvature smoothing
MACMESE'08 Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering
A survey of methods for moving least squares surfaces
SPBG'08 Proceedings of the Fifth Eurographics / IEEE VGTC conference on Point-Based Graphics
Adaptive nonuniform sampling delta modulation: practical design studies
WSEAS Transactions on Circuits and Systems
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Moving least squares (MLS) has wide applications in scattering points approximation fitting and interpolation. In this paper, we improve a novel MLS approach, adaptive MLS, for non-uniform sample points fitting. The size of radius for MLS can be adaptively adjusted according to the consistency of the sampled data points. Experiments demonstrate that our method can produce higher quality approximation fitting results than the MLS.