计算机科学 ›› 2018, Vol. 45 ›› Issue (6): 265-269.doi: 10.11896/j.issn.1002-137X.2018.06.047
张小华, 黄波
ZHANG Xiao-hua, HUANG Bo
摘要: 为提升3D模型几何重构过程的压缩效率,提出一种基于MeTiS网格划分的贝叶斯3D模型几何重构算法。首先,在编码端采用MeTiS方法对原始3D网格进行子网划分,采用随机线性矩阵对子网几何形状进行编码,并对边界节点的邻居节点使用伪随机数生成器进行数据序列构建;然后,利用贝叶斯算法进行几何模型重构算法的设计,在理论上给出了均值、方差矩阵以及模型参数学习规则,实现了3D模型的几何重构;最后,将其与图傅里叶光谱压缩(GFT)、最小二乘压缩(LMS)和基于压缩感知的图傅里叶光谱压缩(CSGFT)等算法进行仿真对比。结果表明,所提方法具有较高的比特率压缩指标以及较低的重构误差,计算效率明显提高。
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