Contour-based surface reconstruction using MPU implicit models

• Published in: Graphical models Vol. 69; no. 2; pp. 139 - 157
• Main Authors: Braude, Ilya, Marker, Jeffrey, Museth, Ken, Nissanov, Jonathan, Breen, David
• Format: Journal Article
• Language: English
• Published: San Diego, CA Elsevier Inc 01-03-2007; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Contours; Exact sciences and technology; Implicit models; Multi-level partition of unity; Normal estimation; Pattern recognition. Digital image processing. Computational geometry; Surface reconstruction; TECHNOLOGY; TEKNIKVETENSKAP; Implicit models; Surface reconstruction; Normal estimation; Multi-level partition of unity; Contours; Statistical analysis; Probabilistic approach; Error bound; Modeling; Adaptive method; Surface normal; Implicit theory; Computer graphics; Curve fitting; Smooth surface
• ISSN: 1524-0703; 1524-0711; 1524-0711
• DOI: 10.1016/j.gmod.2006.09.007

This paper presents a technique for creating a smooth, closed surface from a set of 2D contours, which have been extracted from a 3D scan. The technique interprets the pixels that make up the contours as points in
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and employs multi-level partition of unity (MPU) implicit models to create a surface that approximately fits to the 3D points. Since MPU implicit models additionally require surface normal information at each point, an algorithm that estimates normals from the contour data is also described. Contour data frequently contains noise from the scanning and delineation process. MPU implicit models provide a superior approach to the problem of contour-based surface reconstruction, especially in the presence of noise, because they are based on adaptive implicit functions that locally approximate the points within a controllable error bound. We demonstrate the effectiveness of our technique with a number of example datasets, providing images and error statistics generated from our results.