So far the most precise techniques for digitizing surfaces of real 3D objects employ laser range finders. The acquired range signals are, however, corrupted by noise. Successful surface smoothing can greatly improve the visual appearance of a 3D object, and at the same time can feed improved data to successive processes, such as matching, surface segmentation, and mesh simplification.

The goal is to develop an algorithm that efficiently smooths arbitrarily surfaces represented by a triangle mesh.

We propose a new surface smoothing method based on area decreasing flow, which can be used for preprocessing raw range data or post-processing reconstructed surfaces. Although surface area minimization is mathematically equivalent to the mean curvature flow, area decreasing flow is more efficient for smoothing the discrete surface on which the mean curvature is difficult to estimate. A general framework of regularization based on area decreasing flow is proposed and applied to smoothing range data and arbitrary triangle mesh. Crease edges are preserved by adaptively changing the regularization parameter. The edge strength of each vertex on a triangle mesh is computed by fusing the tensor voting and the orientation check of the normal vector field inside a geodesic window.

• Y. Sun, D. L. Page, J. K. Paik, A. Koschan, and M. A. Abidi, "Triangle mesh-based surface modeling using adaptive smoothing and implicit texture integration," Accepted by 1st Int'l Symposium on 3D Data Processing Visualization and Transmission 2002.
  
  

• J. Shin, Y. Sun, W. Joung, J. Paik, and M. A. Abidi, "Adaptive regularized noise smoothing of dense range image using directional Laplacian operators," in Proc. SPIE, The International Society for Optical Engineering, 2001, vol.4298, pp. 119-126.
  
  

• Y. Sun, J. Paik, J. Price and M. A. Abidi, "Dense range image smoothing using adaptive regularization," in Proc. IEEE Int'l Conf. on Image Processing, 2000, vol.II, pp.744-747.