CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Hybrid Parallel Bundle Adjustment for 3D Scene Reconstruction with Massive
Liu, Xin; Gao, Wei; Hu, Zhanyi
Source PublicationJournal of Computer Science and Technology
2012
Volume27Issue:6Pages:1269-1280
AbstractBundle 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.
KeywordSparse Bundle Adjustment
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15326
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorLiu, Xin
AffiliationNLPR, CASIA
Recommended Citation
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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