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Data-driven quasi-interpolant spline surfaces for point cloud approximation
In this paper we investigate a local surface approximation, the Weighted
Quasi Interpolant Spline Approximation (wQISA), specifically designed for large
and noisy point clouds. We briefly describe the properties of the wQISA
representation and introduce a novel data-driven implementation, which combines
prediction capability and complexity efficiency. We provide an extended
comparative analysis with other continuous approximations on real data,
including different types of surfaces and levels of noise, such as 3D models,
terrain data and digital environmental data
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