Uniformly high order accurate essentially non-oscillatory schemes, 111
Journal of Computational Physics
Efficient implementation of essentially non-oscillatory shock-capturing schemes,II
Journal of Computational Physics
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Efficient implementation of weighted ENO schemes
Journal of Computational Physics
The approximation power of moving least-squares
Mathematics of Computation
Weighted ENO Schemes for Hamilton--Jacobi Equations
SIAM Journal on Scientific Computing
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
Computing and Rendering Point Set Surfaces
IEEE Transactions on Visualization and Computer Graphics
Multi-level partition of unity implicits
ACM SIGGRAPH 2003 Papers
Shape modeling with point-sampled geometry
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2004 Papers
Robust moving least-squares fitting with sharp features
ACM SIGGRAPH 2005 Papers
Error bounds and optimal neighborhoods for MLS approximation
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Surface reconstruction with enriched reproducing kernel particle approximation
SPBG'05 Proceedings of the Second Eurographics / IEEE VGTC conference on Point-Based Graphics
Multiresolution point-set surfaces
GI '08 Proceedings of graphics interface 2008
Technical Section: Variational Bayesian noise estimation of point sets
Computers and Graphics
A singularity-avoiding moving least squares scheme for two-dimensional unstructured meshes
Journal of Computational Physics
Robust Voronoi-based curvature and feature estimation
2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling
Technical Section: Robust normal estimation for point clouds with sharp features
Computers and Graphics
An adaptive moving least squares method for non-uniform points set fitting
ACACOS'10 Proceedings of the 9th WSEAS international conference on Applied computer and applied computational science
Adaptive moving least squares for scattering points fitting
WSEAS Transactions on Computers
The theory and application of an adaptive moving least squares for non-uniform samples
WSEAS Transactions on Computers
ℓ1-Sparse reconstruction of sharp point set surfaces
ACM Transactions on Graphics (TOG)
Feature-Preserving Reconstruction of Singular Surfaces
Computer Graphics Forum
A survey of methods for moving least squares surfaces
SPBG'08 Proceedings of the Fifth Eurographics / IEEE VGTC conference on Point-Based Graphics
Edge-aware point set resampling
ACM Transactions on Graphics (TOG)
SMI 2013: Voronoi-based feature curves extraction for sampled singular surfaces
Computers and Graphics
An adaptive normal estimation method for scanned point clouds with sharp features
Computer-Aided Design
Feature-Preserving Surface Reconstruction and Simplification from Defect-Laden Point Sets
Journal of Mathematical Imaging and Vision
| Hi-index | 0.00 |
In this paper we present a high-fidelity surface approximation technique that aims at a faithful reconstruction of piecewise-smooth surfaces from a scattered point set. The presented method builds on the Moving Least-Squares (MLS) projection methodology, but introduces a fundamental modification: While the classical MLS uses a fixed approximation space, i.e., polynomials of a certain degree, the new method is data-dependent. For each projected point, it finds a proper local approximation space of piecewise polynomials (splines). The locally constructed spline encapsulates the local singularities which may exist in the data. The optional singularity for this local approximation space is modeled via a Singularity Indicator Field (SIF) which is computed over the input data points. We demonstrate the effectiveness of the method by reconstructing surfaces from real scanned 3D data, while being faithful to their most delicate features.