Compression and Transmission of Multi-resolution Clustered Meshes
Author | : International Business Machines Corporation. Research Division |
Publisher | : |
Total Pages | : 10 |
Release | : 1999 |
ISBN-10 | : OCLC:246486660 |
ISBN-13 | : |
Rating | : 4/5 ( Downloads) |
Download or read book Compression and Transmission of Multi-resolution Clustered Meshes written by International Business Machines Corporation. Research Division and published by . This book was released on 1999 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "When working with large geometric models in a client-server environment, it is desirable to generate or store hierarchies of levels of detail (LOD) on the server and to progressively transmit them to the client in a compressed form. In addition to the traditional role of reducing polygon counts to accelerate client frame rates, such an approach permits the LOD hierarchy to be used to reduce the transmission latency. Among the many existing methods for generating multi-resolution polygonal models only a few address progressive transmission, efficient compression, or a combination of the two. With the exception of the Progressive Simplicial Complex (PSC) method, all of the existing compression schemes are limited to manifold meshes with constant topology and all of the existing schemes which handle non-manifold meshes feature little or no compression. In this paper, we introduce a new scheme for compressing and progressively transmitting (potentially non-manifold) LODs generated by traditional vertex clustering algorithms. We refer to these LODs as Multi-Resolution Clustered (MRC) meshes. We extend the Topological Surgery bit-stream syntax to handle MRC meshes. Connectivity information is transmitted from high to low resolution and is followed by the progressive transmission of geometry and property information from low to high resolution. The method is progressive with reasonable transmission latencies, produces compression ratios comparable with the most efficient single-resolution schemes, and is significantly more efficient then [sic] the PSC method."