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The progressive mesh compression based on meaningful segmentation

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Abstract

Nowadays, both mesh meaningful segmentation (also called shape decomposition) and progressive compression are fundamental important problems, and some compression algorithms have been developed with the help of patch-type segmentation. However, little attention has been paid to the effective combination of mesh compression and meaningful segmentation. In this paper, to accomplish both adaptive selective accessibility and a reasonable compression ratio, we break down the original mesh into meaningful parts and encode each part by an efficient compression algorithm. In our method, the segmentation of a model is obtained by a new feature-based decomposition algorithm, which makes use of the salient feature contours to parse the object. Moreover, the progressive compression is an improved degree-driven method, which adapts a multi-granularity quantization method in geometry encoding to obtain a higher compression ratio. We provide evidence that the proposed combination can be beneficial in many applications, such as view-dependent rendering and streaming of large meshes in a compressed form.

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Authors and Affiliations

  1. PDL Laboratory, University of Defense Technology, Changsha City, Hunan province, 410073, China

    Zhi-Quan Cheng, Hua-Feng Liu & Shi-Yao Jin

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  1. Zhi-Quan Cheng
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  2. Hua-Feng Liu
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Correspondence to Zhi-Quan Cheng.

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Cheng, ZQ., Liu, HF. & Jin, SY. The progressive mesh compression based on meaningful segmentation. Visual Comput 23, 651–660 (2007). https://doi.org/10.1007/s00371-007-0128-5

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