  This paper presents a tensor multiplication based smoothing algorithm that
follows a two step denoising method. Unlike other traditional averaging
approaches, our approach uses an element based normal voting tensor to compute
smooth surfaces. By introducing a binary optimization on the proposed tensor
together with a local binary neighborhood concept, our algorithm better retains
sharp features and produces smoother umbilical regions than previous
approaches. On top of that, we provide a stochastic analysis on the different
kinds of noise based on the average edge length. The quantitative and visual
results demonstrate the performance our method is better compared to state of
the art smoothing approaches.
