Previous work on progressive mesh compression focused on triangle meshes but meshes containing other types of faces are commonly used. Therefore, we propose a new progressive mesh compression method that can efficiently compress meshes with arbitrary face degrees. Its compression performance is competitive with approaches dedicated to progressive triangle mesh compression. Progressive mesh compression is linked to mesh decimation because both applications generate levels of detail. Consequently, we propose a new simple volume metric to drive the polygon mesh decimation. We apply this metric to the progressive compression and the simplification of polygon meshes. We then show that the features offered by progressive mesh compression algorithms can be exploited for 3D adaptation by the proposition of a new framework for remote scientific visualization. Progressive random accessible mesh compression schemes can better adapt 3D mesh data to the various constraints by taking into account regions of interest. So, we propose two new progressive random-accessible algorithms. The first one is based on the initial segmentation of the input model. Each generated cluster is compressed independently with a progressive algorithm. The second one is based on the hierarchical grouping of vertices obtained by the decimation. The advantage of this second method is that it offers a high random accessibility granularity and generates one-piece decompressed meshes with smooth transitions between parts decompressed at low and high levels of detail. Experimental results demonstrate the compression and adaptation efficiency of both approaches.
