A Framework for Data-driven Progressive Mesh Compression

Progressive mesh compression techniques have reached very high compression ratios. However, these techniques usually do not take into account associated properties of meshes such as colors or normals, no matter their size, nor do they try to improve the quality of the intermediate decompression meshes. In this work, we propose a framework that uses the associated properties of the mesh to drive the compression process, resulting in an improved quality of the intermediate decompression meshes. Based on a kd-tree geometry compression algorithm, the framework is generic enough to allow any property or set of properties to drive the compression process provided the user defines a distance function for each property. The algorithm builds the kd-tree structure using a voxelisation process, which recursively separates the set of vertices according to the associated properties distances. We evaluate our method by comparing its compression ratios to recent algorithms. In order to evaluate the visual quality of the intermediate meshes, we carried a perceptive evaluation with human subjects. Results show that at equal rates, our method delivers an overall better visual quality. The algorithm is particularly well suited for the compression of meshes where geometry and topology play a secondary role compared to associated properties, such as with many scientific visualization models.