12:30pm
EQuad E415
Title: 3D Geometry Compression Using Mesh Filtering
Abstract: A number of static and multi-resolution methods have been introduced in recent years to compress 3D meshes. In most of these methods the connectivity information is encoded without loss of information, but user-controllable loss of information is tolerated while compressing the geometry and property data. All these methods are very efficient at compressing the connectivity information, but the geometry and property data typically occupies much more room in the compressed bitstream than the compressed connectivity data. In this paper we investigate the use of polynomial linear filtering as a global predictor for the geometry data of a 3D mesh in multi-resolution 3D geometry compression schemes. After encoding the geometry of the lowest level of detail, the geometry at each subsequent level of detail is predicted by applying a polynomial filter to the geometry of its presecessor lifted to the connectivity of the current level. The polynomial filter is design to minimize the l2-norm of the approximation error, but other norms can be used as well. Three properties of the filtered mesh are studied next: accuracy, robustness and compression ratio. The Zeroth Order Filter (unit polynomial) is found to have the best compression ratio. But higher order filters achieve better accuracy and robustness properties as the price of a slight decrease of the compression ratio. This work has been done jointly with Gabriel Taubin (IBM - T.J.Watson Research Center).