Implicit B-Spline Surface Reconstruction

Functional networks for B-spline surface reconstruction

Recently, a new extension of the standard neural networks, the so-called functional networks, has been described [E. Castillo, Functional networks, Neural Process. Lett. 7 (1998) 151–159]. This approach has been successfully applied to the reconstruction of a surface from a given set of 3D data points assumed to lie on unknown Bézier [A. Iglesias, A. Gálvez, Applying functional networks to CAGD...

A Fast and Adaptive Surface Reconstruction Algorithm Based on Implicit Tensor-Product B-Spline Surfaces

Based on the implicit tensor-product B-spline (ITPBS) representation of surfaces, we propose a fast and adaptive algorithm to solve the surface reconstruction problem—reconstructing a surface from a dense set of point clouds. Our algorithm is driven by a surface fitting model proposed in [19], which amounts to solving a quadratic optimization problem. We explore the matrix form of the surface f...

NURBS Surface Reconstruction Using Rational B-spline Neural Networks

Surface reconstruction is a very challenging step in Reverse Engineering. It generates a surface from point cloud acquired from a part surface. NURBS surfaces are commonly used for freeform surface reconstruction. There are several algorithms of NURBS surface reconstruction including: Least Squares Methods, simulated annealing, and particle swarm optimization. The major problem of these methods...

Periodic B - Spline Surface Skinning Of

Meshless Parameterization and B-Spline Surface Approximation

This paper proposes a method for approximating unorganized points in lR 3 with smooth B-spline surfaces. The method involves: meshless parameteri-zation; triangulation; shape-preserving reparameterization; and least squares spline approximation.