Simple and efficient compression of animation sequences
Simple and efficient compression of animation sequences
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DOI：
10.1145/1073368.1073398
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发表时间：
2005-07
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影响因子：
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通讯作者：
Mirko Sattler;Ralf Sarlette;R. Klein
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文献类型：
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作者：
Mirko Sattler;Ralf Sarlette;R. Klein
我们提出了一种新的用于动画的几何压缩方法，该方法基于聚类主成分分析（CPCA）。我们的方法不是分析每一帧的顶点集，而是分析某一动画长度内所有顶点的路径集。因此，使用数据驱动的方法，它能够识别随时间“连贯”的网格部分。这通常会导致将网格非常有效且稳健地分割成有意义的聚类，例如鸡的翅膀。然后使用标准主成分分析（PCA）对这些部分分别进行压缩。与先前的方法相比，这些聚类中的每一个都可以使用更少的PCA成分更有效地进行压缩。结果表明，新方法优于其他压缩方案，如基于纯PCA的压缩或与线性预测编码的组合，同时保持更好的重建误差。即使在传输之前对成分和权重进行量化，情况也是如此。重建过程非常简单，可以直接在GPU上执行。
We present a new geometry compression method for animations, which is based on the clustered principal component analysis (CPCA). Instead of analyzing the set of vertices for each frame, our method analyzes the set of paths for all vertices for a certain animation length. Thus, using a data-driven approach, it can identify mesh parts, that are "coherent" over time. This usually leads to a very efficient and robust segmentation of the mesh into meaningful clusters, e.g. the wings of a chicken. These parts are then compressed separately using standard principal component analysis (PCA). Each of this clusters can be compressed more efficiently with lesser PCA components compared to previous approaches. Results show, that the new method outperforms other compression schemes like pure PCA based compression or combinations with linear prediction coding, while maintaining a better reconstruction error. This is true, even if the components and weights are quantized before transmission. The reconstruction process is very simple and can be performed directly on the GPU.