Abstract
A voxel-based method for flattening a surface while best preserving the distances is presented. Triangulation or polyhedral approximation of the voxel data are not required. The problem is divided into two main subproblems: Voxel-based calculation of the minimal geodesic distances between the points on the surface, and finding a configuration of points in 2-D that has Euclidean distances as close as possible to the minimal geodesic distances. The method suggested combines an efficient voxel-based hybrid distance estimation method, that takes the continuity of the underlying surface into account, with classical multi-dimensional scaling (MDS) for finding the 2-D point configuration. The proposed algorithm is efficient, simple, and can be applied to surfaces that are not functions. Experimental results are shown.KeywordsGeodesic DistanceTexture MappingChain CodeMarching CubeComputational SurfaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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