Progressive geometry compression

A. Khodakovsky, P. Schröder, W. Sweldens
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引用次数: 675

Abstract

We propose a new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning. We observe that meshes consist of three distinct components: geometry, parameter, and connectivity information. The latter two do not contribute to the reduction of error in a compression setting. Using semi-regular meshes, parameter and connectivity information can be virtually eliminated. Coupled with semi-regular wavelet transforms, zerotree coding, and subdivision based reconstruction we see improvements in error by a factor four (12dB) compared to other progressive coding schemes.
渐进几何压缩
我们提出了一种新的渐进式压缩方案,用于几何扫描产生的任意拓扑,高度详细和密集采样的网格。我们观察到网格由三个不同的组成部分组成:几何形状、参数和连接信息。后两者无助于减少压缩设置中的错误。使用半规则网格,参数和连通性信息几乎可以消除。与半正则小波变换、零树编码和基于细分的重构相结合,我们看到与其他渐进式编码方案相比,误差提高了四倍(12dB)。
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