• DocumentCode
    75837
  • Title

    Direct Orthogonal Distance to Quadratic Surfaces in 3D

  • Author

    Lott, Gus K.

  • Author_Institution
    MITRE Corp., McLean, VA, USA
  • Volume
    36
  • Issue
    9
  • fYear
    2014
  • fDate
    Sept. 2014
  • Firstpage
    1888
  • Lastpage
    1892
  • Abstract
    Discovering the orthogonal distance to a quadratic surface is a classic geometric task in vision, modeling, and robotics. I describe a simple, efficient, and stable direct solution for the orthogonal distance (foot-point) to an arbitrary quadratic surface from a general finite 3D point. The problem is expressed as the intersection of three quadratic surfaces, two of which are derived from the requirement of orthogonality of two non-coincident planes with the tangent plane to the quadric. A sixth order single-variable polynomial is directly generated in one coordinate of the surface point. The method detects intersection points at infinity and operates smoothly across all real quadratic surface classes. The method also geometrically detects continuums of orthogonal points (i.e., from the exact center of a sphere). I discuss algorithm performance, compare it to a state-of-the-art estimator, demonstrate the algorithm on synthetic data, and describe extension to arbitrary dimension.
  • Keywords
    computational geometry; arbitrary dimension; arbitrary quadratic surface; direct orthogonal distance; general finite 3D point; geometric task; intersection point detection; noncoincident plane orthogonality; orthogonal point continuum detection; quadratic surface classes; quadratic surface intersection; quadric plane; sixth-order single-variable polynomial; sphere center; surface point coordinate; synthetic data; tangent plane; Approximation algorithms; Approximation methods; Convergence; Polynomials; Three-dimensional displays; Transforms; Orthogonal distance regression; direct methods; foot-point; projective geometry; quadratic surface;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.2014.2302451
  • Filename
    6722938