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A scale-elastic discrete grid structure for voxel-based modeling and management of 3D data
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-08 , DOI: 10.1016/j.jag.2022.103009
Yi Lei, Xiaochong Tong, Tengteng Qu, Chunping Qiu, Dali Wang, Yuekun Sun, Jiayi Tang

Three-dimensional (3D) discrete grid systems have been widely applied to voxel-based modeling and the management of 3D data. In the existing rigid grid structure (RGS), the scales of the RGS-based voxel grid in X/Y/Z dimensions are interrelated. Therefore, the RGS cannot satisfy the anisotropic scale requirements of voxels in different dimensions, rendering RGS-based methods unable to compress the voxel model effectively and manage voxels efficiently. A scale-elastic grid structure (SEGS) was proposed to solve these problems and applied to voxel-based modeling and management. Comparisons with the RGS showed that when the voxel model accuracy remained unchanged, the storage space of the voxel model generated by the SEGS-based method decreased to 12.9% on average, whereas the efficiency of decoding the voxel model increased by 3.34 times on average. The efficiencies of importing, indexing, and querying voxel data by the SEGS-based method increased by 5.27, 6.84, and 3.19 times on average.



中文翻译:

用于基于体素的 3D 数据建模和管理的尺度弹性离散网格结构

三维 (3D) 离散网格系统已广泛应用于基于体素的建模和 3D 数据的管理。在现有的刚性网格结构(RGS)中,基于RGS的体素网格在X/Y/Z维度上的尺度是相互关联的。因此,RGS不能满足不同维度体素的各向异性尺度要求,导致基于RGS的方法无法有效地压缩体素模型和有效地管理体素。为了解决这些问题,提出了一种尺度弹性网格结构(SEGS),并将其应用于基于体素的建模和管理。与 RGS 的比较表明,在体素模型精度不变的情况下,基于 SEGS 的方法生成的体素模型的存储空间平均下降到 12.9%,而体素模型的解码效率平均提高了 3.34 倍。

更新日期:2022-09-08
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