期刊
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
卷 27, 期 6, 页码 1269-1280出版社
SCIENCE PRESS
DOI: 10.1007/s11390-012-1303-3
关键词
sparse bundle adjustment; GPU; compute unified device architecture; structure from motion
资金
- National Natural Science Foundation of China [60835003]
- Chinese Academy of Sciences [XDA06030300]
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 CPUs, 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.
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