Reconstruction and representation of 3D objects with radial basis functions (original) (raw)

Abstract

† (a) (b) Figure 1: (a) Fitting a Radial Basis Function (RBF) to a 438,000 point-cloud. (b) Automatic mesh repair using the biharmonic RBF.

Key takeaways

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  1. Radial Basis Functions (RBFs) enable efficient representation of complex 3D surfaces from large point-clouds.
  2. Fast methods reduce computation time and memory usage, enabling RBF fitting to millions of points.
  3. RBF center reduction allows significant data compression while maintaining high accuracy in surface modeling.
  4. Smooth surface reconstruction from noisy LIDAR data demonstrates RBF's effectiveness in practical applications.
  5. The paper proposes a unified framework for surface interpolation, smoothing, and mesh repair using RBFs.

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