Data-driven quasi-interpolant spline surfaces for point cloud approximation. (June 2020)

• Record Type: Journal Article
• Authors: Raffo, Andrea; Biasotti, Silvia
• Is Part Of: Computers & graphics. Volume 89(2020)
• Page Start: 144
• Page End: 155
• Publication Date: 2020-06
• Subjects: Spline methods -- Quasi-interpolation -- Point clouds -- Noise -- Data-driven model assessment; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2020.05.004
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: A detailed description of the wQISA method when dealing with surface approximation. A multi-level approximation algorithm based on a data-driven weight definition. Comparative analysis of the wQISA outcome with other continuous approximations. Graphical abstract: Abstract: 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.