Multi-resolution wavelet analysis for simplification and visualization of multi-textured meshes

This paper describes a method to reduce multi-texture 3D meshes using a multi-resolution wavelet analysis. Large and dense multi-modal meshes require new methods for efficient display. In this paper, we present a mesh simplification process, that inherently deals with multi-dimensional data sets, controlled in a feature space composed of geometry, curvature, and the textures themselves. The result of the wavelet analysis using a multi-resolution analysis (MRA) based on the 2D quincunx-wavelet transform is considered as a texture map called the `detail relevance'. Virtual range and texture images are captured from selected viewpoints located around the object. The detail extraction is achieved using a multi-resolution approach based on the wavelet cascade analysis. The MRA process extracts detail information at various resolutions and produces a texture image that shows the relevance information attached to each vertex of the mesh. The user has input in this process to select what resolutions are more relevant than others. This approach is useful for filtering noise, preserving discontinuities, mining for surface details, reducing data, and many other applications. We present simplification results of digital elevation maps and 3D objects.