A fast adaptive multipole algorithm in three dimensions
Journal of Computational Physics
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Fast Evaluation of Radial Basis Functions: Methods for Generalized Multiquadrics in $\RR^\protectn$
SIAM Journal on Scientific Computing
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Sparse surface reconstruction with adaptive partition of unity and radial basis functions
Graphical Models - Special issue on SMI 2004
Reconstruction with Voronoi centered radial basis functions
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Meshfree Thinning of 3D Point Clouds
Foundations of Computational Mathematics
Fast multipole methods on graphics processors
Journal of Computational Physics
Meshfree Approximation Methods with MATLAB
Meshfree Approximation Methods with MATLAB
Shape preserving surface reconstruction using locally anisotropic radial basis function interpolants
Computers & Mathematics with Applications
Spectral sampling of manifolds
ACM SIGGRAPH Asia 2010 papers
Extended papers from NPAR 2010: Shape and tone depiction for implicit surfaces
Computers and Graphics
Sketch-based warping of RGBN images
Graphical Models
A two-level approach to implicit surface modeling with compactly supported radial basis functions
Engineering with Computers
A Hierarchical Approach to 3D Scattered Data Interpolation with Radial Basis Functions
CADGRAPHICS '11 Proceedings of the 2011 12th International Conference on Computer-Aided Design and Computer Graphics
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
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We investigate the use of Hermite Radial Basis Functions (HRBF) Implicits with least squares for the implicit surface reconstruction of scattered first-order Hermitian data. Instead of interpolating all pairs of point-normals, we select a small subset of point-normals as centers of the HRBF Implicits while considering all pairs as least-squares constraints. Centers are adaptively sampled via a novel greedy algorithm that takes into account Hermitian data and distances between points. This approach produces sets of centers that are globally well distributed and preserves local features. We show that this yields accurate surface reconstructions with small sets of centers.