Browsing Matematisk institutt by Author "Raffo, Andrea"

Now showing items 1-5 of 5

  • Data-driven quasi-interpolant spline surfaces for point cloud approximation 
    Raffo, Andrea; Biasotti, Silvia (Journal article / Tidsskriftartikkel / AcceptedVersion; Peer reviewed, 2020)
    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 ...
  • Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects 
    Romanengo, Chiara; Raffo, Andrea; Qie, Yifan; Anwer, Nabil; Falcidieno, Bianca (Journal article / Tidsskriftartikkel / AcceptedVersion; Peer reviewed, 2022)
    We propose Fit4CAD, a benchmark for the evaluation and comparison of methods for fitting simple geometric primitives in point clouds representing CAD objects. This benchmark is meant to help both method developers and those ...
  • Mathematical methods for geometry reconstruction and shape analysis 
    Raffo, Andrea (Doctoral thesis / Doktoravhandling, 2022)
    The last decades have witnessed an exponential growth in the amounts of data that are collected, stored, and shared worldwide daily. In many scientific disciplines, information consists of a set of sample points describing ...
  • Reverse engineering of CAD models via clustering and approximate implicitization 
    Raffo, Andrea; Barrowclough, Oliver Joseph David; Muntingh, Agnar Georg Peder (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2020)
    In applications like computer aided design, geometric models are often represented numerically as polynomial splines or NURBS, even when they originate from primitive geometry. For purposes such as redesign and isogeometric ...
  • Weighted quasi-interpolant spline approximations: Properties and applications 
    Raffo, Andrea; Biasotti, Silvia (Journal article / Tidsskriftartikkel / AcceptedVersion; Peer reviewed, 2020)
    Continuous representations are fundamental for modeling sampled data and performing computations and numerical simulations directly on the model or its elements. To effectively and efficiently address the approximation of ...