Multi-Resolution Texture Coding for Multi-Resolution 3D Meshes

We present an innovative system to encode and transmit textured multi-resolution 3D meshes in a progressive way, with no need to send several texture images, one for each mesh LOD (Level Of Detail). All texture LODs are created from the finest one (associated to the finest mesh), but can be re- constructed progressively from the coarsest thanks to refinement images calculated in the encoding process, and transmitted only if needed. This allows us to adjust the LOD/quality of both 3D mesh and texture according to the rendering power of the device that will display them, and to the network capacity. Additionally, we achieve big savings in data transmission by avoiding altogether texture coordinates, which are generated automatically thanks to an unwrapping system agreed upon by both encoder and decoder

Constrained Texture Mapping And Foldover-free Condition

Texture mapping has been widely used in image processing and graphics to enhance the realism of CG scenes. However to perfectly match the feature points of a 3D model with the corresponding pixels in texture images, the parameterisation which maps a 3D mesh to the texture space must satisfy the positional constraints. Despite numerous research efforts, the construction of a mathematically robust foldover-free parameterisation subject to internal constraints is still a remaining issue. In this paper, we address this challenge by developing a two-step parameterisation method. First, we produce an initial parameterisation with a method traditionally used to solve structural engineering problems, called the bar-network. We then derive a mathematical foldover-free condition, which is incorporated into a Radial Basis Function based scheme. This method is therefore able to guarantee that the resulting parameterization meets the hard constraints without foldovers

ITEM: Inter-Texture Error Measurement for 3D Meshes

We introduce a simple and innovative method to compare any two texture maps, regardless of their sizes, aspect ratios, or even masks, as long as they are both meant to be mapped onto the same 3D mesh. Our system is based on a zero-distortion 3D mesh unwrapping technique which compares two new adapted texture atlases with the same mask but different texel colors, and whose every texel covers the same area in 3D. Once these adapted atlases are created, we measure their difference with ITEM-RMSE, a slightly modified version of the standard RMSE defined for images. ITEM-RMSE is more meaningful and reliable than RMSE because it only takes into account the texels inside the mask, since they are the only ones that will actually be used during rendering. Our method is not only very useful to compare the space efficiency of different texture atlas generation algorithms, but also to quantify texture loss in compression schemes for multi-resolution textured 3D meshes

Texturing of multi-resolution meshes with basis meshes

A well known problem in the field of multi-resolution meshes is attribute preservation. To simplify mesh geometry while surface attributes, like texture information, remain stable, the textures have to cover several parameterized triangles (mesh patches). In such cases we have to simplify the surface parameterization along with the geometrical simplification. To minimize visible deviation while simplifying, we need a suitable parameterization and must avoid drastic changes along the patch borders. In this paper we present an algorithm that creates surface patches for multi-resolution meshes. These patches are parameterized to share textures and normal-maps for all possible mesh approximations. To create the patches, we simplify the mesh to a low resolution triangle mesh (basis mesh) whose triangle structure is projected to the original surface in an refinement step. The projected basis triangles are used to build the surface patches. These patches are finally parameterized with shape-preserving weights

A new 3-D mesh simplificatión algorithm

To simplify the 3D color head mesh ,it is more important to keep the boundary and quality of the head’s sense organs including eyes, eyebrows, nose and mouth. In this paper, we present a novel mesh simplification algorithm based on region segmentation. The algorithm can be divided into two stages: segmentation and simplification. After the automatic segmentation of 3D color head mesh into different head parts, vertices are classed into region-boundary vertices and region-inner vertices. Using iterative edge collapse and region-weighted error metric, the algorithm generates continuous levels of detail (LOD). Results of several experiments are shown, demonstrating the validity and efficiency of our method.Keywords: mesh simplification, level of detail, image segmentation, multi-resolution mode