Mesh Denoising based on Normal Voting Tensor and Binary Optimization
This is an incremental improvement for computer graphics and 3D modeling applications.
The paper tackles mesh denoising by proposing a two-step algorithm using a normal voting tensor and binary optimization to compute smooth surfaces, resulting in better retention of sharp features and smoother umbilical regions compared to state-of-the-art methods.
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.