Simultaneous mesh simplification and noise smoothing of range images

We propose a novel algorithm to smooth and simplify simultaneously range images and also triangle meshes derived from those images. These data sets often suffer from noise and over-sampling. To overcome these issues, smoothing from image processing and simplification from computer graphics attempt to minimize noise and reduce complexity, respectively. Typically, these algorithms are separate and distinct steps, but we combine them into one algorithm. We employ surface normal voting to generate robust orientation estimates and then extend the quadric error metric framework to smooth noise while simplifying the surface. We demonstrate the capabilities of this algorithm with both synthetic and real data. The proposed algorithm provides significant noise smoothing improvement when compared to the standard Garland and Heckbert (1998) quadric simplification algorithm.