Hybrid Parallel Bundle Adjustment for 3D Scene Reconstruction with Massive

	Hybrid Parallel Bundle Adjustment for 3D Scene Reconstruction with Massive
	Liu, Xin; Gao, Wei; Hu, Zhanyi
发表期刊	Journal of Computer Science and Technology
	2012
卷号	27 期号:6 页码:1269-1280
摘要	Bundle adjustment (BA) is a crucial but time consuming step in 3D reconstruction. In this paper, we intend to tackle a special class of BA problems where the reconstructed 3D points are much more numerous than the camera parameters, called Massive-Points BA (MPBA) problems. This is often the case when high-resolution images are used. We present a design and implementation of a new bundle adjustment algorithm for efficiently solving the MPBA problems. The use of hardware parallelism, the multi-core CPUs as well as GPUs, is explored. By careful memory-usage design, the graphic-memory limitation is effectively alleviated. Several modern acceleration strategies for bundle adjustment, such as the mixed-precision arithmetics, the embedded point iteration, and the preconditioned conjugate gradients, are explored and compared. By using several high-resolution image datasets, we generate a variety of MPBA problems, with which the performance of five bundle adjustment algorithms are evaluated. The experimental results show that our algorithm is up to 40 times faster than classical Sparse Bundle Adjustment, while maintaining comparable precision.
关键词	Sparse Bundle Adjustment
收录类别	SCI
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15326
专题	多模态人工智能系统全国重点实验室_机器人视觉
通讯作者	Liu, Xin
作者单位	NLPR, CASIA
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Liu, Xin,Gao, Wei,Hu, Zhanyi. Hybrid Parallel Bundle Adjustment for 3D Scene Reconstruction with Massive[J]. Journal of Computer Science and Technology,2012,27(6):1269-1280.
APA	Liu, Xin,Gao, Wei,&Hu, Zhanyi.(2012).Hybrid Parallel Bundle Adjustment for 3D Scene Reconstruction with Massive.Journal of Computer Science and Technology,27(6),1269-1280.
MLA	Liu, Xin,et al."Hybrid Parallel Bundle Adjustment for 3D Scene Reconstruction with Massive".Journal of Computer Science and Technology 27.6(2012):1269-1280.

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