Surface Reconstruction from Dense Point-Sets Using Image-Processing Techniques

Current methods for extracting surface-models from “clouds of points” use standard CAGD techniques and guarantee neither fairness nor closeness of fit — important requirements for applications like Aesthetic Surface Design (Styling) and Reverse Enginnering. This paper elaborates on a new approach, combining CAGD with Image Processing, to derive surface-reconstruction algorithms that can handle dense range-images and meet strict industrial specifications.

[1]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Paul J. Besl.  Geometric signal processing , 1989 .

[3]  W. Gander,et al.  Fitting of circles and ellipses: Least squares solution , 1994 .

[4]  Colin Bradley,et al.  G1 continuity of B-spline surface patches in reverse engineering , 1995, Comput. Aided Des..

[5]  Paul J. Besl,et al.  Surfaces in Range Image Understanding , 1988, Springer Series in Perception Engineering.

[6]  Joe Warren,et al.  Approximation of dense scattered data using algebraic surfaces , 1991, Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences.

[7]  Ulrich Dietz.  Erzeugung glatter Flächen aus Meßpunkten , 1995 .

[8]  Paul J. Besl,et al.  Direct construction of polynomial surfaces from dense range images through region growing , 1995, TOGS.

[9]  Xue Yan,et al.  Neural network approach to the reconstruction of freeform surfaces for reverse engineering , 1995, Comput. Aided Des..

[10]  Jake K. Aggarwal,et al.  Range image understanding , 1992, Image and Vision Computing.

[11]  Nickolas S. Sapidis,et al.  Variable-Order Surface Reconstruction Through Region Growing , 1995, CAD Systems Development.

[12]  Chia-Hsiang Menq,et al.  Smooth-surface approximation and reverse engineering , 1991, Comput. Aided Des..