A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data

  • Authors:
  • Volker Blanz;Albert Mehl;Thomas Vetter;Hans-Peter Seidel

  • Affiliations:
  • Max-Planck-Institut für Informatik, Saarbrücken, Germany;Ludwig Maximilians University, München, Germany;University of Basel, Switzerland;Max-Planck-Institut für Informatik, Saarbrücken, Germany

  • Venue:
  • 3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
  • Year:
  • 2004

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Abstract

General information about a class of objects, such as human faces or teeth, can help to solve the otherwise ill-posed problem of reconstructing a complete surface from sparse 3D feature points or 2D projections of points. We present a technique that uses a vector space representation of shape (3D Morphable Model) to infer missing vertex coordinates. Regularization derived from a statistical approach makes the system stable and robust with respect to noise by computing the optimal tradeoff between fitting quality and plausibility. We present a direct, non-iterative algorithm to calculate this optimum efficiently, and a method for simultaneously compensating unknown rigid transformations. The system is applied and evaluated in two different fields: (1) reconstruction of 3D faces at unknown orientations from 2D feature points at interactive rates, and (2) restoration of missing surface regions of teeth for CAD-CAM production of dental inlays and other medical applications.