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针对大规模点集三维重建问题的分布式捆绑调整方法

DOI: 10.3724/SP.J.1004.2012.01428, PP. 1428-1438

Keywords: 捆绑调整,计算机集群,图形处理器,运动相机重建三维结构

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Abstract:

?捆绑调整(Bundleadjustment,BA)是三维重建中的关键步骤,它需要消耗大量的计算时间和内存存储空间.本文旨在处理三维点数比相机模型数多很多的捆绑调整问题,我们称之为针对大规模三维点集的捆绑调整(Massive-pointsbundleadjustment,MPBA)问题.此类问题在对高分辨率图像进行三维重建时会经常出现.为了高效地解决MPBA问题,本文提出一种分布式的捆绑调整算法.通过基于三维点集划分的分解方法,原MPBA问题被分成若干子问题.该分解方法不依赖于输入参数的内在联系,因而分解结果与具体BA问题无关.算法被映射于两个集群上,一个集群有5台计算机,另一个集群有3台计算机,其中每台机器都配置一块图形处理器(Graphicprocessingunit,GPU).通过对若干MPBA问题的实验,与经典捆绑调整算法SBA(Sparsebundleadjustment)相比,本文算法获得了最高达75倍的加速比,并保持了算法的高精确度.而且,本文算法的两个实现所消耗的单机内存存储空间,仅为SBA实现的1/7和1/4.

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