【摘要】 捆绑调整(Bundle adjustment,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-points bundleadjustment,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(Graphic processing unit,GPU).通过对若干MPBA问题的实验,与经典捆绑调整算法SBA(Sparse bundle adjustment)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.更多还原

【Abstract】 Bundle adjustment(BA) is a crucial step in 3D scene reconstruction but time and memory consuming.In this paper,we try to tackle a frequently encountered BA problem where the reconstructed 3D points are more numerous than the camera parameters,namely massive-points BA problem.This is often the case when high-resolution images are used.We present a novel distributed bundle adjustment(DBA) algorithm for e?ciently solving the massive-points BA problem,where the original BA problem is divided into sub-problems by partitioning the 3D reconstructed points.Such a partition scheme is in dependent of the input parameters,it could be applied to various BA problems.Two specific implementations,one on a shared memory cluster of 5 computers and the other on a cluster of 3 GPU(Graphic processing unit)-integrated computers,are developed.These implementations are experimentally compared with the classical singlethread sparse bundle adjustment(SBA).Experimental results show that our algorithm is up to 75 times faster than SBA,while maintaining comparable precision.And the one-computer memory requirements of these two implementations are just 1/7 and 1/4 of the original SBA.更多还原