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Efficient surface reconstruction using generalized coulomb potentials.

Andrei C Jalba1, Jos B T M Roerdink

  • 1Institute for Mathematics and Computing science, University of Groningen, The Netherlands. andrei@cs.rug.nl

IEEE Transactions on Visualization and Computer Graphics
|October 31, 2007
PubMed
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This study introduces a new surface reconstruction method using generalized Coulomb potentials. It effectively handles noisy and sparse point clouds, offering a robust and efficient solution for detailed surface generation.

Area of Science:

  • Computer Vision
  • Computational Geometry
  • Scientific Computing

Background:

  • Surface reconstruction from point clouds is challenging due to noise and sparsity.
  • Existing methods often struggle with outliers and lack global information.
  • Orientation information is frequently unavailable in real-world datasets.

Purpose of the Study:

  • To develop a novel, geometrically adaptive method for surface reconstruction.
  • To address limitations of existing techniques in handling noisy and sparse data.
  • To achieve robust and detailed surface generation without requiring orientation information.

Main Methods:

  • A fast convection algorithm is employed to guide the evolving surface towards data points.
  • Generalized Coulomb potentials are used to generate a force field for surface convection.

Related Experiment Videos

  • An adaptive octree grid and a hierarchical algorithm enable efficient potential evaluation.
  • The reconstruction is formulated as a convection problem in a velocity field.

    • Main Results:

    • The method demonstrates high resilience to shot noise and outliers.
    • Global information from all data points is effectively utilized via long-range Coulomb potentials.
    • Spatial and temporal complexities are proportional to object size, ensuring favorable speed and flexibility.
    • Experiments confirm the ability to produce crisp, detailed, yet smooth surfaces from sparse and noisy data.

    Conclusions:

    • The proposed convection-based surface reconstruction method offers significant advantages in noise resilience and global information integration.
    • Its adaptive nature and efficient computation make it a flexible and fast alternative to existing approaches.
    • The method successfully reconstructs high-quality surfaces from challenging, real-world point cloud data.