Mesh Denoising based on Normal Voting Tensor and Binary Optimization

TL;DR

This paper introduces a novel tensor-based mesh denoising algorithm that effectively preserves sharp features and smooth regions, outperforming existing methods through a binary optimization approach and stochastic noise analysis.

Contribution

The paper proposes a new normal voting tensor and binary optimization technique for mesh denoising, enhancing feature preservation and smoothing quality.

Findings

Outperforms state-of-the-art smoothing methods in quality.

Effectively preserves sharp features and smooth regions.

Provides stochastic analysis of noise types.

Abstract

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.