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Compression of massive models by efficiently exploiting repeated patterns

We propose a new compression algorithm for massive models, which consist of a large number of small to medium sized connected components. It is by efficiently exploiting repetitive patterns in the input model. Compared with the similar work by finding repetitive patterns, our new algorithm is more efficient on detecting repeated components by recognizing instances repeating in various scalings. We also propose an efficient compression scheme for transformation data. As a result, it can achieve a considerably higher compression ratio.