1 / 9

Multiresolution Analysis of Irregular Meshes with Appearance Attributes

This study explores multiresolution analysis of irregular meshes with appearance attributes, focusing on theoretical and practical applications. Key objectives include level-of-detail decomposition, frequency content extraction, and attribute management, with a focus on filter, denoising, and segmentation. The work encompasses a complete understanding of Guskov decomposition, proposing a new, easier-to-implement method that is faster and less memory-consuming. Attribute analysis and denoising using Wiener filtering have been achieved, with ongoing work on soft thresholding and anisotropic filtering. Feature detection like sharp edges and fast mesh comparison are also in progress. The research culminates in view-dependent visualization and texture analysis, intended for publication in technical reports, SPIE conference papers, and journal articles at various levels.

raglin
Download Presentation

Multiresolution Analysis of Irregular Meshes with Appearance Attributes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multiresolution Analysis of Irregular Mesheswith Appearance Attributes Michaël Roy

  2. Objectives • Theoretical • Level-of-detail decomposition • Frequency content extraction • Attribute management • Practical • View / performance dependent visualization • Filtering / denoising • Segmentation / feature detection • Comparison

  3. Work done • Complete understanding of Guskov decomposition • New decomposition proposed • Easier to implement • Faster • Less memory consuming • More efficient (disjoint frequency bands) • Attribute analysis • Denoising using Wiener filtering

  4. Work to do • Denoising using soft thresholding (isotropic) • Denoising using anisotropic filtering • Feature detection such as sharp edges • Fast mesh comparison (missing parts) • View-dependent visualization • Texture analysis • Technical report • SPIE conference paper • Journal paper

  5. Level 31 Level 25 Level 20 Level 15 Level 10 Results: LOD Decomposition

  6. Initial model Geometric analysis Color analysis Results: Attribute Analysis

  7. Initial model Our method Results: Denoising using Wiener Filtering

  8. Results: Denoising using Soft Thresholding Initial model Noisy model Filtered model

  9. Results: Sharp Edge Detection Clean model Noisy model

More Related