Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations.

ABSTRACT: Recently, spectral methods have been extensively used in the processing of 3D meshes. They usually take advantage of some unique properties that the eigenvalues and the eigenvectors of the decomposed Laplacian matrix have. However, despite their superior behavior and performance, they suffer from computational complexity, especially while the number of vertices of the model increases. In this work, we suggest the use of a fast and efficient spectral processing approach applied to dense static and dynamic 3D meshes, which can be ideally suited for real-time denoising and compression applications. To increase the computational efficiency of the method, we exploit potential spectral coherence between adjacent parts of a mesh and then we apply an orthogonal iteration approach for the tracking

SUBMITTER: Arvanitis G 

PROVIDER: S-EPMC8321066 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature