This paper presents an objective structural distortion measure which reflects the visual similarity between 3D
meshes and thus can be used for quality assessment. The proposed tool is not linked to any specific application
and thus can be used to evaluate any kinds of 3D mesh processing algorithms (simplification, compression,
watermarking etc.). This measure follows the concept of structural similarity recently introduced for 2D image
quality assessment by Wang et al.1 and is based on curvature analysis (mean, standard deviation, covariance) on
local windows of the meshes. Evaluation and comparison with geometric metrics are done through a subjective
experiment based on human evaluation of a set of distorted objects. A quantitative perceptual metric is also
derived from the proposed structural distortion measure, for the specific case of watermarking quality assessment,
and is compared with recent state of the art algorithms. Both visual and quantitative results demonstrate the
robustness of our approach and its strong correlation with subjective ratings.
In this paper we present a new framework, based on subdivision surface fitting, for high rate compression and coding of 3D models. Our algorithm fits the input 3D model, represented by a polygonal mesh, with a piecewise smooth subdivision surface represented by a coarse control polyhedron. Our fitting scheme, particularly suited for meshes issued from mechanical or CAD parts, aims at getting close to the optimality in terms of control points number, while remaining independent of the connectivity of the input mesh. The found subdivision control polyhedron is much more compact than the original mesh and visually represents the same shape after several subdivision steps, without artifacts or cracks, like traditional lossy compression schemes. This control polyhedron is then encoded specifically to give the final compressed stream. Experiments conducted on several 3D models have proven the coherency and the efficiency of our framework, compared with existing compression methods.