Efficient approximation of range images through data-dependent adaptive triangulations

This paper presents an efficient algorithm for generating adaptive triangular meshes from dense range images. The proposed technique consists of two stages. First, a quadrilateral mesh is generated from the given range image. The points of this mesh adapt to the surface shapes represented in the range image by grouping in areas of high curvature and dispersing in low-variation regions. The second stage splits each quadrilateral cell obtained before into two triangles. Between the two possible flips, it is chosen the one whose diagonal's direction is closest to the orientation of the discontinuities present in that cell. Both stages avoid costly iterative optimization techniques. Results with renal range images are presented. They show low CPU times and accurate triangular approximations of the given images.